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1. Annals of the
University of North Carolina Wilmington
International Masters of Business Administration
http://csb.uncw.edu/imba/

2. AN EMPIRICAL INVESTIGATION OF THE IMPACT OF REVIEWS ON MOVIE
REVENUES
Cin Dy Ee
A Thesis Submitted to the
University of North Carolina in Partial Fulfillment
of the Requirements for the Degree of
Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2011
Approved by
Advisory Committee
Peter Schuhmann Lisa Scribner
Joseph Farinella
Chair
Accepted by
_____________________________
Dean, Graduate School

3. ii
TABLE OF CONTENTS
ABSTRACT...................................................................................................................................iii
ACKNOWLEDGEMENTS........................................................................................................... iv
LIST OF TABLES......................................................................................................................... vi
LIST OF FIGURES ...................................................................................................................... vii
CHAPTER 1: INTRODUCTION AND CONTEXT...................................................................... 1
CHAPTER 2: LITERATURE REVIEW........................................................................................ 5
2.1 IMPACT OF MOVIE REVENUES ............................................................................................. 5
2.2 IMPACT OF CRITICS ............................................................................................................. 8
2.3 IMPACT OF GENRE, MPAA RATING .................................................................................. 10
2.4 IMPACT OF CONSUMERS’ DECISION-MAKING.................................................................... 11
2.5 IMPACT OF THE RELEASE DATE OF THE FILM .................................................................... 14
CHAPTER 3: METHODOLOGY................................................................................................ 15
3.1 MODEL .............................................................................................................................. 15
3.2 EXPLANATION OF EACH VARIABLE ................................................................................... 15
3.2.1 Dependent Variables .................................................................................................. 16
3.2.2 Independent Variables................................................................................................ 16
3.3 CRITICS’ RATINGS ............................................................................................................. 17
3.4 DATA SOURCE................................................................................................................... 18
3.5 CRITICS ............................................................................................................................. 18
3.5.2 Rotten Tomatoes........................................................................................................... 19
CHAPTER 4: DISCUSSION ON RESULTS AND MAIN FINDINGS ..................................... 25
CHAPTER 5: SUMMARY AND CONCLUSIONS.................................................................... 29
REFERENCES ............................................................................................................................. 31

4. iii
ABSTRACT
This paper empirically investigates the impact of critics‟ reviews on movie revenues.
Three critics‟ reviews are adopted as categories of independent variables in this research, for
instance, Rotten Tomatoes, Roger Ebert and Metacritic. The researcher conducted several studies
on forecasting to classify the predictors of box-office revenues for the motion picture industry.
Furthermore, it examines how the key factors such as production budget, genre, Motion Picture
Association of America (MPAA) ratings and critics‟ reviews influence the success of movies.
Three dependent variables including total domestic gross revenues, total foreign gross revenues
and domestic opening weekend are used.

5. iv
ACKNOWLEDGEMENTS
First and foremost I would like to give my sincerest gratitude to my committee chairman,
Dr. Joseph Farinella who has helped tutor me and gave useful guidance and advise throughout
my work. I attribute the level of my Masters degree to his effort and time, he is one simply could
not wish for a friendlier and better supervisor.
I acknowledge my second graduate committee; Dr. Peter Schuhmann who has provided
me a good statistics skill and valuable suggestions in the more efficient methods. I also
acknowledge my third graduate committee, Dr Lisa Scribner for her detailed comments and
advice. I appreciate all of your time and help in assisting my work.
Special thanks go to my boyfriend, Steven Keith Farrior, who has supported me
throughout my thesis with his patience, knowledge and motivation. In my daily work, I have
been blessed to have him be with me, with his great assistance and cheerful attitude. The entirety
of my paper has been completed with him who has often had to bear with my frustration, and
keep me in a productive mindset against all negative thoughts. I really appreciate all that he has
done.
I would also like to thank the faculty staffs from Cameron School of Business and the
department of finance for providing the equipment and support that I have needed to complete
my thesis.
Thanks also go to my helpful and friendly IMBA fellow friends, Liu Xin and Cameron
Douglas for their acquaintance and friendship.

6. v
Finally, I would like to thank my family members, especially my parents for constantly
encouraging and supporting me throughout all my overseas studies at University level, giving me
an opportunity to study overseas to gain knowledge and pursue two master degrees.

7. vi
LIST OF TABLES
Table Page
1: Descriptive Statistics..................................................................................................... 21
2: Correlation Coefficients................................................................................................ 23
3: Regressions by Critic reviews categories on Domestic Grosses .................................. 25
4: Regressions by Critic reviews categories on Foreign Grosses ..................................... 27

8. vii
LIST OF FIGURES
Table Page
1: Worldwide Box Office.................................................................................................... 2
2: Box Office Summer Movie Statistics............................................................................. 4

9. CHAPTER 1: INTRODUCTION AND CONTEXT
The motion picture industry possesses a high profile and a highly variable revenue
stream, (Simonoff & Sparrow, 2000). In 2011, moviegoers spent an estimated $280 million
during Memorial Day weekend at the U.S box office, which pushed box-office receipts to a
record high, breaking the previous high of $255 million set back in 2007 (Kaufman, 2011).
A newly released report from the Motion Picture Association of America (MPAA)
illustrated that global box office sales reached a record high in 2010. The Hollywood‟s
international box office sales increased 13% in 2010, compared to 2009 with the largest growth
in Latin America and the Asia Pacific region. This growth drives the worldwide box-office
receipts for all movies released around the world in 2010, achieving an all-time high of $31.8
billion, an increase of 8% over 2009 (Verrier, 2011).
The U.S/ Canadian market repeated its peak performance during 2010, but remained flat
at $10.6 billion. Moreover, China remains a highly restrictive market for foreign film
distribution. Nonetheless, more than 40% of the Asia Pacific box office growth occurred in
China (MPAA 2011). The “Theatrical Market Statistics” for 2010 demonstrate that the motion
picture industry is still performing well, in spite of the economic downturn, shifting business
models, ongoing impact of digital theft, and the ever-increasing levels of piracy (Duke, 2011).
Derived from Figure 1 below, movie studios are intensely interested in predicting the
motion picture revenues as well as the popular nature of the product results with great interest in
gross revenues from the general public. The response of interest here is examining the
relationship between the total U.S domestic gross revenue for each film and predictor variables
such as critics‟ reviews, budget, MPAA ratings and genre. The U.S box-office revenue

10. 2
classically creates the “value” of the film for the other markets, (Ainslie et al, 2005) so we will
examine the foreign grosses globally.
Figure 1: Worldwide Box Office
Source: (Theatrical market statistics, 2010)
Furthermore individual variables, which have an impact on movie financial performance
will be evaluated in this paper. Variables including critics‟ ratings, genre, MPAA rating,
production budget, foreign and domestic total grosses (in millions of dollars) will be studied.

11. 3
Given these variables, we aim to answer several questions, specifically, what role does
each play in determining the total box office gross revenue of a film? Are certain types of films
more or less likely to be moneymakers? Do big budget films make more money?
Several authors1
have claimed that the role of critics is the most prominent in the movie
industry. From The Wall Street Journal 2001, approximately one of every three moviegoers said
they choose films because of favorable reviews. Therefore, the following hypotheses will
summarize the possible links among critics‟ roles and box office revenue:
H1: Domestic opening weekend gross revenues in the US are a predictor of total domestic gross
revenues.
H2: Domestic opening weekend gross revenues in the US are a predictor of total foreign gross
revenues.
H3: Critics‟ reviews are a predictor of total domestic gross revenues.
H4: Critics‟ reviews are a predictor of foreign gross revenues.
According to Hollywood.com, the attendance in 2011 was nowhere in close proximity to
the record levels set in 2002, although the 3-D premium helped to boost higher average ticket
prices, which also incurred an increase in revenues. Figure 2 illustrates that in the summer of
2011 the “theater-going experience has managed to hold its own against the continuing onslaught
of competing technologies and content delivery options” (Dergarabedian, 2011).
1
Eliashberg and Shugan (1997); Holbrook (1999); West and Broniarczyk (1998).

12. 4
Figure 2: Box Office Summer Movie Statistics
Source: (Hollywood.com, 2011)

13. CHAPTER 2: LITERATURE REVIEW
2.1 Impact of Movie Revenues
A number of researchers have been modeling the process of how film revenue is
generated, which has been the subject of many papers in economics and marketing literature
(Chang and Ki, 2005). Most of these models focus on predicting the revenue function for a film
and designing the predictors of box office revenue for motion picture (Chang and Ki, 2005).
Several articles estimate the demand for motion pictures2
. Researchers focus on the
observed seasonality in box-office revenues3
that reveals both seasonality in underlying demand
for movies and seasonality in the number and quality of available movies. This observed
seasonal pattern of sales is a combination of seasonality in underlying demand and seasonal
variation in the quality of movies released. The summer and Christmas seasons generally have
the highest gross revenues.
Ainslie et al (2005) uses a diffusion model for modeling the box office. They consider
seasonal effects and estimate revenue for each movie. The U.S. box- office revenue generally
predicts revenue in foreign markets. Movie studios consider themselves proficient in predicting
movie success. However, the movie industry generally relies more on instinct and analysis by
anecdote as opposed to formal modeling, (Herring, 1998).
2
Mulligan and Motiere (1994), Prag and Casavant (1994), Sawhney and Eliashberg (1996), De Vany and Wells
(1996, 1997), Eliashberg and Shugan (1997), Vanderhart and Wiggins (2001), Nelson et al. (2001), and Moul
(forthcoming).
3
Radas and Shugan (1998, 1999), as well as Vogel (1994) emphasize on the observed seasonality of box-office
revenues.

14. 6
Ainslie et al (2005) implement a combination of a sliding-window logit model and a
gamma diffusion pattern with parameters modified to increase interpretability. They adopted a
Bayesian framework, which has massively categorical variables to both pool information across
movies and extract information from the large number of studios involved with movie release.
Using this approach, they properly accounted for the set of movies available in the box office at
any given time which enables them to provide a better fit of the data and lead to a better
understanding of the drivers of movie market share. Besides that, Chance et al (2005) address
several unique technical issues which include: (1) the requirement that the total revenue function
be non-decreasing over time and (2) the lack of observations on the innovation at the time
release. All models focus on forecasting revenue as a function of characteristic of the film and
certain time series properties.
Over the last 20 years, financial engineers have created an extraordinary assortment of
instruments designed to manage the financial risk associated with films. And these tools take the
form of securitized, equity-like claims on film revenues as well as options on those revenues.
Therefore, Chance et al (2005) introduce a deterministic model of adoption that forms the basis
for the stochastic model and investigates the revenue growth.
Elberse and Eliashberg (2003) make use of the information from the domestic film
performance in order to predict foreign revenues. They model the dynamics of a movie, which
has already been released, based on its revenue in prior weeks. Furthermore, Goetzmann, Pons-
Sanz and Ravid (2004) develop a model for explaining the price paid for movie scripts and the
role of script prices in predicting a movie‟s financial success.

15. 7
They are interested in whether or not a distribution company that has shown previous
financial success is more likely to have future box office hits (Chen, 2002). There is an example
of Eliashberg et al. (2000) predicting revenue and the effect of changes in advertising spending
based on surveys of potential moviegoers who view trailers, (Ferrari and Rudd, 2008).
An article of a parsimonious model for forecasting gross box-office revenues of motion
picturesSawhney & Eliashberg (1996) states that the cumulative box-office revenues can be
predicted by using or testing a more parsimonious model with reasonable accuracy forecasts of
the gross box-office revenues of new motion pictures based on early box office data.
According to Clement, et al (2007) the demand for a new movie is highly uncertain as
movies are “experiential” products, and it is hard for consumers to assess the quality of a movie
until they have experienced it. As consumers can only wisely discuss a film with friends when
they have experienced a movie.
Given the importance of new movies and the uncertainty in predicting the box-office
performance of these new movies, the value of precise box-office forecasts is extremely high in
the motion picture industry. Thus, there is a necessity to give movie exhibitor chains like
Cineplex Odeon and United Artists with a comparatively simple and accurate forecasting tool
that can assist them in maximizing the yield from their exhibition capacity in multiplex theaters
(Elberse & Eliashberg, 2003).
This can help the exhibitor chains in making exhibition decisions beyond the initial
contractual periods of three to five weeks that are based on week-to-week negotiations between
exhibitors and distributors. The extension of the movie is based on the box-office performance

16. 8
(Elberse & Eliashberg, 2003). Additionally, Sharda and Delen (2005) concentrate on forecasting
the financial success of new motion pictures based on a forecasting model.
2.2 Impact of Critics
In recent years, several scholars have expressed a great deal of interest in understanding
the role of critics in film markets, (Cameron 1995 and Caves 2000). Many researchers4
claim
that critics play an important role in consumers‟ decisions in many industries. However, the role
of critics is the most prominent in the movie industry5
. Many empirical studies test the
relationship between critics‟ reviews and box office revenues (Ravid, 1999). The studies pay
special attention to the question of “how much influence critics have on the success of hedonic
products already has been addressed in the motion picture industry” (Clement et al, 2007:78).
The Wall Street Journal (2001) claims that Americans actively seek the advice of movie critics.
In addition, Ravid‟s (1999) study tests the impact of critical reviews on domestic gross
revenue. Eliashberg and Shugan (1997) and Basuroy et al (2003) states that film critics are not
only act like influencers, but also predictors of the success of reviewed films.
Furthermore, Basuroy et al (2003) specify the problems that are relevant to the effects of
critics‟ reviews on box office success. The first issue is the role of critics in affecting box office
performance, and that includes two potential roles of critics: (1) influencers, who actively
influence the decisions of consumers in the before the movies release, and (2) predictors, who
merely predict consumers‟ decisions. More recent research in the film industry suggests that
critics can correctly predict the box office performance without influencing it; also critics‟
4
Austin (1983); Cameron (1995); Caves (2000); Einhorn and Koelb (1982); Eliashberg and Shugan (1997); Goh and
Ederington (1993); Greco (1997); Holbrook (1999); Vogel (2001); Walker (1995).
5
Eliashberg and Shugan (1997); Holbrook (1999); West and Broniarczyk (1998).

17. 9
reviews can influence the consumer‟s decision of whether to watch a movie (Eliashberg and
Shugan, 1997).
On the other hand, researchers constantly have found differential impacts of positive and
negative critics‟ information on audience behavior. They also found that both positive and
negative reviews are correlated with weekly box office revenue over an eight-week period, thus
it proves that critics play dual roles; which can both influence and predict box office outcomes
(Basuroy et al, 2003).
In order to better test the hypotheses among critics‟ roles and box office revenue,
Eliashberg and Shugan‟s (1997) study the correlated data of both positive and negative reviews
with weekly box office revenue. They exemplify this point by considering three different
examples of correlation between weekly box office revenue and critical reviews. Wyatt and
Badger (1984) design experiments using positive, mixed, and negative reviews, and find
audience interest to be compatible with the direction of the review.
The second issue is whether positive and negative reviews have comparable effects on
box office performance. A comparison between the positive impact of good reviews and negative
impact of bad reviews could verify the evidence of negativity bias6
. By doing this, it appears that
negative reviews hurt box-office performance more than positive reviews help box-office
performance.
Two studies lend further support to this idea. The first study, Yamaguchi (1978)
proposes that consumers tend to accept negative reviews more easily than they accept positive
reviews. The second study, suggests that the negativity bias operates in affective processing of
6
Skowronski and Carlston (1989)

18. 10
whether the movie is considered as good or bad (Ito et al., 1998). Therefore, these studies
propose that a critic‟s negative review hurt the box office performance than a critic‟s positive
review helps the box office performance (Basuroy et al, 2003).
Moreover, some analysis-based evidences shows the aggregate impact of critical reviews
on actual box office revenues and the potential role critics play in determining and predicting the
commercial box office performance of motion pictures, (Jehoshua Eliashberg and Steven M.
Shugan, 1997). Thus, there are several motion picture-related studies that will be mentioned in
this paper, such as research on consumer behavior and movies, research on empirical studies on
performance of movies and research on movie “experts” and critic‟s reviews.
In addition, Ravid (1999) mentions in the previous study that star power has received
considerable attention in the literature but stars are not significant predictors of financial success.
He argues that the use of weekly data is critical, thus he extends his study by developing a cross-
sectional model to predict the box-office revenue. His model has rating, release date, number and
quality of reviews along with several other measures, which includes the information that would
not be available before the film is released. He tested the model on a sample of 180 movies
released during the period of 1991-1993.
2.3 Impact of Genre, MPAA Rating
Ainslie et al. (2005) shows that releasing a movie contemporaneously with other movies
of the same genre adversely hurts box-office sales all around. Whereas, releasing a movie against
movies of the same Motion Picture Association of America (MPAA) rating affects its sales in the
beginning, but there is a displacement effect, whereby the long run loss of sales is less severe.

19. 11
Rather than modeling the time to decide and the time to act as exponential decays, they
present a metaanalysis on three parameters to study factors that boost movie sales like MPAA
rating and movie genre. Furthermore, Chang (1975) factor analyzes critic ratings and finds three
types: elites, auteurists, and entertainers. Hsu (2006) investigates the effect of genre on appeal as
measured by the number and favorableness of online reviews.
Also, several reviews of the factors considered by various workers appear in Elberse and
Eliashberg (2003) and in Terry et al. (2005). Ravid (1999) summarizes various studies on the
influence of individual factors in movie financial performance such as stars, sequels, rating, and
budget. The quantitative research on the determinants of movie success has generally used linear
models.
2.4 Impact of Consumers‟ Decision-Making
Sawhney and Eliashberg (1996) built the BOXMOD model. They decompose the
consumers‟ movie selection into two steps: (1) the consumer makes the decision to see a movie
and (2) the consumer acts on this decision. Sawhney and Eliashberg (1996) use a time-series
model to measure the consumer‟s decision to view a movie and to act on that decision.
Chance et al. (2005) find that by developing a stochastic model based on the notion that
an individual‟s decision to purchase the product is driven by two factors: the systematic effect of
others who have already purchased the product and an idiosyncratic effect independent of the
actions of others.
In order to achieve reasonable estimations of five input parameters requested in the
model, they build an econometric model of movie revenues. They use a panel regression model,

20. 12
they estimate the parameters based on films‟ characteristics, the timing of release, and marketing
of the film. Then, by using the first few weeks of revenue data for the film, it provides important
information and can be used with a Kalman filter to update the parameter estimates. Preliminary
empirical tests are conducted using weekly data on revenues generated from latest movies.
A growing number of researchers7
have turned to the idea that market simulations could
aggregate information that traders privately hold to gauge market-wide expectations or classify
„winning concepts‟ in the eyes of consumers (Elberse and Anand, 2006).
Elberse and Eliashberg (2003) design a dynamic simultaneous-equations model of the
drivers and interrelationship of the behavior of consumers and distributor “middle-men”. They
put a strong emphasis on the significance of considering the endogeneity and simultaneity of
consumers and distributor “middle-men” behavior, and challenge conventional wisdom on the
determinants of box office performance. According to (Wanderer, 1987; cited in Eliashberg and
Shugan, 1997), “ critics‟ tastes are similar to consumer tastes as reported in Consumer Union
magazine. ”
In an article on film critics and whether they are influencers or predictors, Eliashberg and
Shugan (1997: 69), provide an experiential view of consumption regarding consumption as “a
primarily subjective state of consciousness with a variety of symbolic meanings, hedonic
responses, and aesthetic criteria”. Also, this experiential perspective involves the studying of
consequences of consumption in terms of the fun, enjoyment, and pleasure obtained from the
experience. Consumers‟ response is a central concept in the experiential view; emotional effects
such as fantasies, images, and arousal obtained from using the products (Hirschman, 1982).
7
Chan, Dahan, Lo and Poggio (2001); Dahan and Hauser (2001); Forsythe, Nelson, Neumann and Wright (1992);
Forsythe, Rietz and Ross (1999); Gruca (2000); Hanson (1999); Spann and Skiera (2003); Wolfers and Zitzewitz
(2004), Surowiecki (2004).

21. 13
Eliashberg and Sawhney (1994) developed a model of the movie-going experience,
which permitted researchers to forecast an individual moviegoer‟s satisfaction level for a film
prior to viewing it. Also, Hirschman and Holbrook (1982) developed the role of the “mental-
imagery” effort expended by consumers in hedonic consumption experiences and in determining
consumption preferences.
Anast (1967) examines differential movie appeals as a correlation of attendance and
finds that eroticism and violence correlate positively with attendance and adventure correlates
negatively (Anast, 1967; cited in Eliashberg and Shugan, 1997). Also, based on early data points,
we could forecast the ultimate success of box office performance of motion pictures.
Thus, in the paper “A Parsimonious Model for Forecasting gross Box-Office Revenues of
Motion Pictures”, Sawhney and Eliashberg (1996) follow Berg‟s (1981) study, drawing upon a
queuing theory framework to conceptualize stochastically the consumer‟s movie adoption
process in two steps, (1) the time to decide to see the new movie, and (2) the time to act on the
adoption decision. The parameter for the time-to-decide process captures the intensity of
information flowing from diverse information sources, while the parameter for the time-to-act
process is related to the delay created by limited distribution intensity and other factors.
However, a key characteristic of MOVIEMOD offered by Eliashberg et al, (2000) is that
it accounts for word-of-mouth (WOM) interactions among potential moviegoers and spreaders
by using the interactive Markov chain representation. Thus, it can investigate whether accounting
for WOM interaction enhances the model performance by calculating the forecast of attendance
that does not account for WOM. MOVIEMOD is a prerelease evaluation system for motion

22. 14
pictures with a number of significant features and can also be applied to a broader set of
managerial decision settings.
An important finding from the empirical testing is that the modeling framework,
BOXMOD-I measures the time to decide and the time to act that can characterize the adoption
process (Sawhney and Eliashberg, 1996). BOXMOD-I model produces moderately accurate
early forecasts, using at most the first three weeks of data for calibration and the predictive
performance of the model compares favorably with benchmark models (Sawhney and
Eliashberg, 1996).
2.5 Impact of the Release Date of the Film
Several studies focus on forecasting gross revenues at the movie box office. Chen (2002)
is paying attention to whether a movie was released at a time when more people go to theaters,
for instance, during the summer or holiday weekends, and Christmas season. This former is
predominantly vital as the first weekend of a movie‟s release is normally a strong determinant of
the total gross, yet it is at the peak attendance (Chen, 2002).

23. CHAPTER 3: METHODOLOGY
3.1 Model
Gross Revenue = α0 + α1 Ti + α2 R1 + α3 M1 + α4 (Dummies for Genre + Dummies for Ratings +
Budget)
Ti = Tomatoes rating, R1 = Roger Ebert, M1 = Metacritic
Where:
The domestic gross revenue is estimated by the function of critic reviews, genre, ratings and
budget. The Ti variable represents reviews of Rotten Tomatoes, and the R1 variable represents
reviews of Roger Ebert, then the M1variable represents reviews of Metacritic. This model
focuses on forecasting total revenue as a function of characteristics of the film and three critics‟
reviews by Rotten Tomatoes, Roger Ebert and Metacritic.
3.2 Explanation of Each Variable
The variables in this study are critical reviews (Metacritic, Roger Ebert, Rotten
Tomatoes), Budget (millions), genre, MPAA rating (R, PG, PG-13 and G) and year. They are
used as categories of independent variables. This is because they are significantly related to the
total box office performance. Three categories of dependent variables are adopted in this
analysis: total domestic gross revenues, total foreign gross revenues, and domestic opening
weekend.

24. 16
3.2.1 Dependent Variables
Total domestic gross revenues: This paper uses both total domestic and total foreign
gross revenues. Both domestic and foreign grosses have been paying attention for the predictions
of box office revenues.
Total foreign gross revenues: This variable is selected in order to test whether some
independent variables affect the four dependent variables to different degrees.
Domestic opening weekend gross revenues: The opening weekend is considered to be
highly correlated with total domestic gross revenues.
3.2.2 Independent Variables
Budget: The results from Table 2 show the correlation coefficients of production budget
have not significantly influenced the foreign and domestic revenues on the movie gross. The
budget data are gathered from the box office mojo. Due to the lack of information on budget is
considered confidential and thus some movies were eliminated in the paper.
Genre: The genre has 37 sub-categories that are combined into seven main categories.
The movies are categorized into the following seven genres: Action, Adventure, Animation,
Comedy, Drama, Horror and Sci-Fi. Musical genre was omitted in the dummy variables. The
data of genre is gathered from the box-office mojo, some of them are taken from Metacritic.
These two sources provided available information on genre. Furthermore, some types of movie
genre have been given attention as a predictor of box office receipts.

25. 17
MPAA rating: The content rating is assigned by the MPAA, which has been considered
as a significant factor to the movie industry, (Ravid, 1999). This is because the rating tends to
determine the potential size of the audience. There are four rating categories: R, PG, PG-13 and
G are used in the analysis as dummy variables. Among the four categories, G is the dummy
variable excluded from the MPAA ratings and PG-13 has the largest potential audience with its
highest dummy variable.
3.3 Critics‟ ratings
According to Austin (1983), critics aid individuals in making a movie choice,
understanding the movie‟s content, developing an initial opinion of the film and communicating
movie information to others. The critics‟ rating has been broadly tested by previous research,
(Ravid, 1999) and has been classically supported, (Ravid, 1999). The critics‟ review in this paper
are gathered from three nationally recognized sources: Metacritic, Roger Ebert and Rotten
Tomatoes. Roger Ebert has a different scale, using the star rating for each movie (1-4 stars),
while Metacritic and Rotten Tomatoes are based on a 100 percentage score for all movies.
Release date of the film. Several authors have used the release dates for the box office
predictions. The rationale is that a high-attendance-period release (e.g., Christmas) attracts a
bigger audience, which leads to higher box office performance. The release periods vary, so in
this paper, the release dates are changed to numbers; from 1-41. For instance, 1972 is considered
as 1, so 1975 is counted as 4. For example, the year of 2009 is given a value of 39.

26. 18
3.4 Data Source
This paper analyzes the impact of reviews on movie revenues, measuring a movie‟s
success by the gross revenues of opening weekend movies for both domestic and foreign
markets. The data is collected from box office mojo (www.boxofficemojo.com), a website for
movie reviews and movie information.
A dataset of movies that earned $100 million to $700 million of total U.S gross revenues
and released from 1972-2011 are selected for analysis. However, some movies are eliminated
limited available data. This paper sums up the release date of the film into yearly movies and
texting as an independent variable. Eighty-three movies are eliminated due to insufficient
information. Thus, 217 movies are used for the final analysis, narrowed down from 300.
In addition, data are collected based on a wide range of other film characteristics such as
critical reviews, genre, budget and MPAA rating. Due to the massive scale of genre, the
researcher has to narrow down the scale into seven categories of genre. Therefore, dummy
variables were created for the following classifications: action, sci-fi, comedy, animation, horror,
adventure and drama. The omitted dummy variable in this test was the musical genre.
3.5 Critics
3.5.1 Metacritic
Metacritic is a website which accumulates a multitude of reviews on specific games,
movies, TV shows and music albums. They calculate a weighted average from the most
respected critics writing reviews online and in print or movies and video games. The weigh
reflects the clout of reviewers or the primary sources (Jacsó, 2004). It was established in January

27. 19
2001 by March Doyle, Jason Dietz and Julie Roberts. They convert the review score into a
percentage while many review websites give a review score out of five, out of ten or out of a
hundred (Jacso, 2004).
Their website aims to help consumers make an informed decision about how to spend
their time and money on movies. Their belief is that multiple opinions are better than one. It is
similar to RottenTomatoes site, with more movies from more sources (Jacsó, 2004). The
public‟s voice can be as significant as the critics, and the opinions are then scored for easy
measurement.
The following shows the range of scores on metascores:
0-19: Overwhelming dislike
20-39: Generally unfavorable
40-59: Mixed or average reviews
60-80: Generally favorable reviews
81-100: Universal acclaim
3.5.2 Rotten Tomatoes
The critic website, Rotten Tomatoes (www.rottentomatoes.com), was used as a
supplementary source of critical reviews. Brewer et al. (2009) discovered a strong link between
Rotten Tomatoes reviews and gross box-office revenues. The website compiles movie reviews
by critics and converts them into a percentage, taking into consideration the percentage of critics
who recommend each flic.
A good review, meaning the critic recommends the film, is illustrated as a fresh tomato;
while a bad review, one that isn‟t recommended, is illustrated by a rotten tomato. A film must

28. 20
obtain at least a compiled score of 60 percent to have a fresh tomato. The site uses dummy
variables to calculate a fresh or rotten rating. Thus, Rotten Tomatoes is widely known as a film
review aggregator and the ultimate movie database (Brewer et al., 2009)
Senh Dong is the creator of Rotten Tomatoes, he launched this website on August 12,
1999 as a spare time project. The objective of creating Rotten Tomatoes was to create a website
where people can get access to reviews from an assortment of critics in the U.S. (Wikepedia,
2011)
He is a fan of Jackie Chan, and this inspired him to start collecting all the reviews of
Chan's movies as they were coming out in the United States. Moreover, this website was an
immediate success, receiving mentions by Yahoo, Netscape and USA Today within the first
week of its launch; it attracted "600 - 1000 daily uniques" (Wikepedia, 2011)
Its name derives from the cliché of audiences throwing tomatoes and other vegetables at a
poor stage performance. The company is currently owned by Flixster, which is a social movie
site allowing users to share movie ratings, discover new movies, and meet others with similar
tastes in movies. However, Flixster itself is owned by Warner Bros, which is an American
producer of film and television entertainment, since May 2011 (Wikepedia, 2011).
They use a combination of professional reviews and audience reactions, and gather
reviews from various movie critics, and use the ratio of positive to negative reviews to provide
an overall “freshness” rating. This website is a reliable resource for deciding what movie to see
(Brewer et al.,2009).

29. 21
3.5.3 Roger Ebert
Roger Ebert, born on June 18th
, 1942, is an American film critic, journalist and
screenwriter. He obtained public awareness from his film review column in the Chicago Sun-
Times since 1967, after that he switched to online (Digital journal, 2011).
Forbes characterized him as “the most powerful pundit in America”, and also the first
film critic that won the Pulitzer Price for Criticism in 1975, (Digital journal, 2011). He has been
widely syndicated by his television programs and has been nominated for numerous Emmy
awards. Universal Press syndicated his movies in more than 200 newspapers in the United States
and across the world in 2010.
The reviews from his website are based on a star rating. He awards four stars to the
highest quality movies and a half star to those of the lowest quality. He gives no stars for those
movies that he thinks are completely unworthy (Digital journal, 2011).
Table 1: Descriptive Statistics
Variables Mean Median
Standard
Deviation
Min Max
Domestic Grosses 216,635,943.68 184,134,515.00 90,568,897.08
128,200,21
7.00
760,507,625
.00
Foreign Grosses 282,262,993.64 232,600,000.00 212,756,025.53
14,752,800
.00
2,021,767,5
47.00
Domestic
Opening
Weekend
50,049,729.25 46,522,560.00 28,666,999.91 598,257.00
158,411,483
.00
Metacritic 63.15
63.00 16.23 20.00 100.00
Rotten Tomatoes 67.19
72.00 23.18 6.00 100.00
Roger Ebert 2.89 3.00 0.83 0.50 4.00
Budget (millions) 99.15 90.00 59.25 5.00 270.00
Year 32.23 33.00 7.24 1.00 41.00

30. 22
Table 1 cont.
Action 0.50 0.00 3.67 0.00 54.00
Adventure 0.27 0.00 1.98 0.00 29.00
Animation 0.30 0.00 2.25 0.00 33.00
Comedy 0.43 0.00 3.20 0.00 47.00
Drama 0.12 0.00 0.91 0.00 13.00
Horror 0.08 0.00 0.64 0.00 9.00
Sci-Fi 0.28 0.00 2.05 0.00 30.00
PG 0.47 0.00 3.46 0.00 51.00
PG-13 1.03 1.00 7.57 0.00 112.00
R 0.36 0.00 2.66 0.00 39.00
(Note. Year = the year of the movies are released during the period of 1972-2011.)
Standard deviation is defined as the positive square root of the variance and is frequently
used as a quantitative measure of risk, (Investopedia, 2011). Table 1 shows the difference among
each variable. From the table above, the maximum domestic gross revenue is $760,507,625 and
the minimum domestic gross revenue is 128,200,217. The maximum foreign gross revenue is
$2,021,767,547, which is much higher than the domestic grosses.
Rotten Tomatoes‟ standard deviation is 23.18, which makes it have the largest range of
scores out of the three sites reviewed. While the standard deviation of Metacritic is 16.23, range
from 20-100. And Roger Ebert has the lowest standard deviation at 0.83.

31. 23
Table 2: Correlation Coefficients
Table 2 Correlation Coefficients
RE RT M GDR GFR Budget Opening
RE 1
RT 0.70514 1
M 0.68573 0.90490 1
GDR 0.21174 0.27232 0.31581 1
GFR 0.17977 0.18444 0.24636 0.74338 1
Budget -0.12622 -0.08897 -0.03683 0.29736 0.50357 1
Opening -0.19405 -0.12665 -0.07760 0.48325 0.45379 0.63092 1
*RE = Roger Ebert
*RT = Rotten Tomatoes
*M = Metacritic
*GDR = Gross Domestic Revenue
*GFR = Gross Foreign Revenue
*Opening = Opening Weekend
In Table 2, the correlation coefficients show that Rotten Tomatoes and Metacritic is
highly correlated with its positive coefficient 0.90490. For instance, Avatar‟s score is 83 percent
from Rotten Tomatoes and Metacritic. Also, the data appears that the coefficient between
domestic gross revenue and budget has a coefficient 0.29736. The coefficient is 0.50357 between
foreign gross revenue and budget.
The results show that critic reviews and the budget has a coefficient of (0.12622) for
Roger Ebert, (0.08897) for Rotten Tomatoes, and (0.03683) for Metacritic. It‟s clearly
impossible for critics‟ reviews to have a direct or indirect impact on a movie‟s budget because
critic reviews always come at the end of the movie process while budget is in the beginning.
Though, budget has an indirect relation with critics‟ reviews because for example, a higher

32. 24
budget possibly enables a movie to possess better CGI, better actors/actresses, etc. which in-turn
would increase critics scores.
Furthermore, currently the results have led to believe all four of the hypotheses incorrect.
Firstly, hypothesies one stated that the domestic opening weekend gross revenues are a predictor
of total domestic gross revenues. Consistent with the findings above, domestic opening weekend
gross revenues have a correlation with domestic gross revenues at a coefficient of 0.48325, thus
H1 is not supported.
Moreover, domestic opening weekend gross revenues display a coefficient of 0.45379
with the foreign gross revenues. Thus, hypotheses two that stated the domestic opening weekend
gross revenues are a predictor of total foreign gross revenues is foreseen as incorrect as well.
Though, all of these hypotheses will have to be further tested because none currently are proven
completely incorrect.
Additionally, Table 2 shows that all three critics‟ reviews have a low correlation
coefficient with the total domestic and foreign revenues. Therefore, critics‟ reviews do not have a
significant impact on the gross domestic revenues, or the foreign gross revenues. Moreover, the
findings have shown that if the reviews increase by 1, gross domestic revenue will only go up
0.21174.
However, one interesting finding was that domestic gross revenues are positively related
to foreign gross revenue with a high coefficient of 0.74338. This explains that total domestic
gross revenues have a high prediction power on total foreign gross revenue.

33. CHAPTER 4: DISCUSSION ON RESULTS AND MAIN FINDINGS
Gross Revenue = α0 + α1 Ti + α2 R1 + α3 M1 + α4 (Dummies for Genre + Dummies for Ratings +
Budget)
Ti = Rotten Tomatoes rating, R1= Roger Ebert, M1= Metacritic
Table 3: Regressions by Critic reviews categories on Domestic Grosses
Variables Model 1 Model 2 Model 3 Model 4
Intercept 21996411.04
0.36
28065352.58
0.47
101359385.2
1.73
81958460.21
1.43
Metacritic 2029309.14
2.48
2093441.37
5.80
RT -112729.29
-0.20
1283809.85
5.00
RE 6062837.90
0.65
29561809.85
4.12
Budget 486773.87
3.75
484401.80
3.76
509189.54
3.81
507120.52
3.87
Action 21766527.97
0.52
23646929.8
0.57
2140885.65
0.05
18464114.9
0.44
Adventure 43065872.87
1.03
44579908.62
1.08
34019081.07
0.79
43724115.6
1.04
Animation 14957662.29
0.33
16379043.93
0.37
15901326.62
0.34
19211167.78
0.42
Comedy 55553665.99
1.35
57698857.84
1.41
33042148.11
0.79
55248180.89
1.33
Drama 15440022.01
0.35
19895887.16
0.46
-1863046.91
-0.04
22783084.93
0.52
Horror 21598135.61
0.46
24315432.7
0.52
535577.86
0.01
23146947.19
0.49
Sci-Fi 94001737.14
2.21
95928614.85
2.27
76309682.2
1.74
90373273.05
2.10
PG 23510343.99
0.90
22534626.7
0.87
16503083.4
0.62
12528479.42
0.48
PG-13 11685182.84
0.39
9516591.33
0.32
16587019.65
0.54
3638947.22
0.12
R -28211065.8
-0.86
-30162678.9
-0.93
-23100993.8
-0.69
-39024232.50
-1.18
Year -1194302.55
-1.25
-1201686.9
-1.27
-1676228.7
-1.72
-1235448.7
-1.28

34. 26
Table 3 above shows the regression results with each variable on total domestic gross
revenues. There is a strong trend in the link between high budget and high revenue. Take Avatar
for example, it had a large budget that enabled them to produce stunning graphics, which in turn
attracted a larger audience, and boosted the revenues.
Typically, when the production budget of a given movie goes up, revenue will go up in-
turn. In Table 3, Sci-fi movies were found to have the most popular ratings in the domestic
market. People enjoy watching Sci-fi movies, so they typically have higher revenues due to the
requirement for a large budget to enable eye-popping CGI (Computer Generated Images) like the
hit movie Transformers.
Films deemed rated R by the MPAA tend to have less revenue compared to G, PG and
PG-13, because they are limited to people who are of age seventeen and above This means that
those individuals who are under 17 will not be allowed to watch R-rated motion pictures,
narrowing the target market, thus causing the revenue to be cut down. Thus, the effects of
MPAA ratings were found in Table 3 that R was negatively related to total domestic gross
revenues.
In addition, the results in Table 3 indicate that Roger Ebert has the strongest results
among all three reviews; he has the largest impact of reviews on domestic gross revenues. The
data shows that when Roger‟s ratings increase by 1, the domestic revenue will rise up $2.9
million. However, Rotten Tomatoes has the least impact of reviews on domestic grosses.

35. 27
Table 4: Regressions by Critic reviews categories on Foreign Grosses
Variables Model 1 Model 2 Model 3 Model 4
Intercept -191,142,934.26
-1.45
-146,417,311.04
-1.12
-39,659,235.45
-0.31
-86,961,213.67
-0.70
Metacritic 3,684,079.24
2.06
3,425,148.69
4.29
RT -1,267,893.21
-1.04
1,923,799.92
3.40
RE 39,555,834.58
1.95
63,826,616.57
4.17
Budget 1,707,873.68
6.02
1,701,065.28
5.98
1,736,247.93
6.00
1,750,258.22
6.13
Action -144,715,926.59
-1.59
-132,413,830.29)
-1.45
-143,476,412.20
-1.54
-166,322,572.00
-1.82
Adventure 12,921,875.12
0.14
23,051,753.37
0.25
20,853,281.37
0.22
3,730,887.90
0.04
Animation -28,181,543.13
-0.29
-18,864,128.99
-0.19
-12,913,146.11
-0.13
-26,382,041.64
-0.27
Comedy -69,818,734.14
-0.78
-54,002,403.19
-0.60
-61,957,663.89
-0.68
-90,963,953.55
-1.01
Drama -123,244,687.94
-1.29
-92,702,146.73
-0.98
-88,788,941.01
-0.92
-138,111,217.88
-1.45
Horror -90,737,192.80
-0.88
-71,329,145.57
-0.69
-76,347,524.28
-0.73
-110,256,293.79
-1.07
Sci-Fi 32,803,083.59
0.35
44,763,439.89
0.48
33,883,129.88
0.36
12,115,305.29
0.13
PG 86,239,078.01
1.51
77,127,033.94
1.35
60,013,730.52
1.04
72,156,362.08
1.26
PG-13 126,959,512.90
1.93
110,586,355.54
1.69
101,457,161.85
1.52
127,073,688.67
1.93
R 103,988,944.77
1.45
87,291,931.33
1.22
74,449,274.83
1.02
100,880,264.24
1.41
Year -57,149.80
-0.03
-12,291.85
-0.01
-224,487.45
-0.11
-422,797.31
-0.20
Previously, Table 3 represents the regressions of total domestic gross revenues have
found several significant variables such as Roger‟s rating, production budget, genre and MPAA
ratings. And all these significant variables in Table 3 are also significant in the Table 4 with total
in the foreign gross revenues.

36. 28
In Table 4, the data shows that Roger Ebert‟s rating is highly correlated with movie gross
revenues, especially in foreign markets. Surprisingly, Roger‟s ratings have a stronger influence
on the revenue of foreign grosses than domestic grosses. The foreign grosses coefficient is $6.3
million compared to the domestic grosses coefficient of $2.9 million. This means that when
Roger‟s ratings increase by 1, foreign gross revenue will increase $6.3 million.
In foreign countries, movies rated PG-13 by the MPAA are the most popular compared to
the most popular for domestic, which is PG. The Sci-fi genre in foreign countries is also popular
just as it is in the domestic market. Other genres such as comedy, drama, action and horror are
not as popular in the foreign market as they are in the domestic market. This may be related to
cultural differences.

37. CHAPTER 5: SUMMARY AND CONCLUSIONS
This paper examines the impact of reviews on both domestic movie gross revenues and
foreign movie gross revenues in an effort to investigate which variable affects a movie. The
empirical part of this paper has addressed the research questions through the regression models.
Regression models have given the results concerning to not only the key factors of impact on
movies revenues, but also the impact of critics‟ reviews.
Based on the findings, Table 3 and 4 show that the regressions of Roger‟s ratings appear
to be the strongest predictor that affect both domestic and foreign revenues, especially in foreign
markets. Although, the results in Table 2 show that all three reviews are not strongly correlated
with both domestic gross revenues and foreign gross revenues. However, the results are followed
by the regressions. Therefore, this paper proved that critics‟ reviews serve as key informants to
box-office performance; it impacts both domestic and foreign revenues. Yet Roger‟s ratings are
proven as a significant predictor of both domestic and foreign gross revenues among all three
critics‟ reviews.
The findings have shown that total domestic gross revenues were significantly and
positively related to the total foreign gross revenues. Hence, the total domestic gross revenues
are contributed to the total foreign gross revenues. By using the methodology in this paper, the
results suggest that both domestic and foreign revenues affect domestic opening weekend
revenues.
The results could offer a way to help identify what factors movie executives consider to
increase the box-office revenues and explain how critical reviews impact the gross box-office
revenues in the U.S and foreign markets. In this sense, further research could classify how large

38. 30
budget films would have more profit than the small budget films, and also explain how some
movies achieve and maintain revenue even in low budget films.

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