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Reviews:

Do They Matter Less For Sequels?

Erniël de Boer

10248064

Bachelor Thesis

BSc in Economics and Business, Business Studies Faculty of Economics and Business

Supervisor:

Dr. Frederik B. Situmeang Academic Year: 2016-2017

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Statement of Originality

This document is written by Erniël de Boer, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The motion picture business is an industry in which enormous amounts of money are involved, and thus, where bad decisions can lead to huge losses. To minimize these risks, researchers have searched and discovered factors which would positively affect box office revenues. This research focusses on the already established positive effect on box office revenue, caused by the signaling of quality through consumer reviews and expert reviews, and in particular the difference in its effect between franchise parent movies and sequels. It is theorized that because parent movies have certain advantages over sequels due to the spill-over effect, this would lead to the positive relationship between reviews and revenue being weaker for sequels. The hypotheses, focusing on the different aspects of consumer and expert reviews, rating score and frequency, were tested through quantitative data analysis on 417 franchise movies. However, the findings revealed no evidence to support the hypotheses. The results did indicate the opposite to be true, as the effect of reviews on sequels seemed stronger instead of weaker compared to parent movies. However, further research is necessary to confirm these findings.

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Table of Content

1. Introduction ... 1

2. Theoretical Framework ... 3

2.1 Experiential goods and signaling theory ... 3

2.2 Franchise movies and sequels ... 3

2.1.2 Brand extension and its spill-over effect ... 4

2.1.3 Franchise movies and sequels performance ... 4

2.3 Consumer reviews and expert reviews ... 5

2.1.1 Consumer reviews ... 5

2.1.2 Expert reviews ... 6

2.1.3 Difference between consumer and expert reviews ... 6

2.4 Reviews’ effect on sequels ... 7

2.5 Production Budget ... 7

2.6 Box office performance... 7

2.7 Hypotheses ... 8 3. Methodology ... 9 3.1 Data source ... 9 3.2 Dependent variable ... 9 3.3 Independent variables ... 9 3.3.1 Sequels ... 9

3.3.2 Consumer and expert reviews ... 10

3.3.3 Production Budget ... 10

3.4 Methods ... 10

4. Results ... 11

4.1 Descriptive Statistics ... 11

4.1.1 Franchise movies ... 11

4.1.2 Parent movies vs. sequels ... 11

4.2 Correlations ... 12

4.2.1 Franchise Movies ... 12

4.2.2 Parent movies... 13

4.2.3 Sequel movies ... 13

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4.3 Hypothesis testing ... 14 4.3.1 Regression model ... 14 4.3.2 Consumer reviews ... 15 4.3.3 Expert reviews ... 16 4.3.3 Sub-questions ... 16 5. Discussion ... 17 5.1 Results ... 17 5.2 Tested Hypotheses ... 17 5.2.1 Hypotheses ... 18 5.2.2 Sub-question... 19 5.3 Managerial Implications ... 20

5.4 Limitations and future research ... 20

5.5 Conclusion ... 21

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1. Introduction

In Hollywood, it has been generally assumed that the success or failure of each feature film is fairly hard to predict (Sochay, 1994). As the average production and marketing costs for movies has risen to $106,6 million in 2007 (MPAA, 2007), movie studios seek ways to minimize their risks. One way how studios try to deal with this issue, is by producing sequels and/or pursuing successful franchises (Eliashberg, Elberse & Leenders, 2006). Franchise movies and sequels have been proven to be profitable and low-risk in the last couple of decades (Sood & Drèze, 2006). For example: nine out of the ten highest-grossing movies of 2011 in the U.S. were part of a franchise (Yong, Tie-nan, Xiang-Yang, 2013).

Furthermore, there are factors that already have been uncovered as drivers and moderators of success for feature films. These are factors such as budgets/production costs (Prag & Casavant, 1994), popular actors (Basuroy, Chatterjee, & Ravid, 2003) and media publicity (Wang, Zhang, Li & Zhu, 2010). Two of the more recently emerged determinants of success are online consumer reviews (Liu, 2006) and expert reviews (Reinstein & Snyder, 2005). Additionally, these factors can function as a signal to consumers, indicating if the movie is of high or low quality, and thus influencing their movie going behavior (Basuroy, Desai & Talukdar, 2006; Basuroy & Chatterjee, 2008; Sood & Drèze, 2006; Situmeang, Leenders & Wijnberg, 2014).

A number of studies have already displayed the function of online consumer and expert reviews, and as a whole they display the strong effect it has on the box office revenue of movies (Kim, Park & Park, 2013). The consumer and expert reviews were usually divided into two different aspects: the total amount of reviews (the frequency) and its rating score, which both had differing effects between each kind of reviews (Basuroy et al., 2003; Liu, 2006).

Franchise movies generally have a couple of advantages over non-franchise movies. Sequels in a franchise usually profit from the spill-over effect created by the brand of the parent movies, and from prior released sequels, as people who liked the parent movie or prior sequel, are more likely to go see the sequel (Elberse & Eliashberg, 2003). Additionally, sequels are generally shown in a significantly larger number of theaters than non-sequels (Dhar, Sun & Weinberg, 2012), and their brand awareness is boosted since the brand has already been introduced (Balachander and Ghose, 2003). Additionally, having a larger number of intervening films helps sequels and build the overall franchise, because of the buzz and anticipation created by the prior films (Basuroy & Chatterjee, 2008).

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There already has been extensive research done on the effects of consumer and expert reviews on box office success of feature films, but not so much on its effect on franchise movies. In this research the focus will lie upon how consumer and expert reviews of franchise movies influence their box office success. Furthermore, because of the above mentioned benefits that affect sequels, it could be possible that the effect of consumer and expert reviews on box office success is weaker for sequels in franchises than for its parent movies.

The spill-over effect of franchise movies might influence the effect of reviews, leading to the question of: What is difference in effect of consumer and expert reviews between parent movies and sequels?

This research will use 143 movie franchises, consisting of a total of 417 movies, which were released internationally between 1972 and 2016, to estimate the effect of the frequency and rating scores of consumer and expert reviews on franchise movies.

The expectation is that, while both consumer and expert reviews positively affect franchise movies’ box office success, the reviews have a weaker effect on the box office success of sequels, than on parent movies.

In the next section the theoretical framework is explored by describing and reviewing the current literature on the subject, and the hypotheses are formulated. Then the data and methods are described, followed by the results of the model estimation and analysis. Finally, the paper closes with its conclusions and a discussion for future research.

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2. Theoretical Framework

In this section the theoretical framework behind the research is established. This is done by first reviewing current literature which examines important concepts and theories concerning this subject. Starting with experiential goods and the signaling theory, and franchise movies, sequels and brand extension. Then, consumer and expert reviews are analyzed, followed by the effect of reviews on sequels, production budget and box office performance. Finally, concluding with the formulation of the hypotheses.

2.1 Experiential goods and signaling theory

Movies are products that are experiential (Sood & Drèze, 2006), which can be defined as goods of which the quality cannot be determined by inspection, but need to be bought or consumed to learn its quality (Wernerfelt, 1988; Liu 2006). Because movie tickets must be bought before seeing it, and movies are judged in terms of their enjoyment value (Basuroy, Desai & Talukdar, 2006), consumers will look for and rely on signals which help them establish an idea about the product’s quality. This theory is defined as the signaling theory (Basuroy & Chatterjee, 2008), which is based upon the reduction of information asymmetry between consumers and movie studios (Kirmano & Rao, 2000). According to Basuroy et al. (2006) Studios will have an incentive to provide signals of good-quality movies, which should attract more consumers, while consumers are motivated to look out for these signals, to form their quality perception. Credible signals about a movie from its studio will have a positive effect on the perceived quality by consumers, thus lead to higher box office revenue (Basuroy et al., 2006).

Different kinds of factors which could function as signals of quality, have been studied in the case of motion pictures. Such as, star power and production budget, where the casting of popular actors or high production budgets signal quality (Basuroy & Chatterjee, 2008). Sood and Drèze (2006) found that sequel movies signal a successful parent movie, which in its turn signals that the sequel must be of quality as well. Expert reviews and consumer reviews have also been determined as signaling quality, since they pass on judgement and information (Situmeang, Leenders & Wijnberg, 2014).

2.2 Franchise movies and sequels

Each year, for the last 8 years (2008-2015), at least 5 out of the top 10 highest grossing movies in the U.S. market were sequels; and of the top 10 highest domestic grossing movies of all-time, 8 are part of a franchise (Box Office Mojo). Considering this, it is obvious why movie studios pursue successful franchises and the sequels that come with it (Eliashberg, Elberse & Leenders, 2006). Along those

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lines, researchers have been studying franchise movies and sequels in an attempt to discover the reason behind their frequent popularity and success.

2.1.2 Brand extension and its spill-over effect

In recent years, the way Hollywood is branding their movies has become similar to the way in which consumer-packaged-goods manufacturers brand their goods (Sood & Drèze, 2006). One of the reasons for this way of branding could be explained by the study done by Aaker and Keller (1990). They found that positive attitudes towards a product of a certain brand, transmits to products that share the same brand, because it signals similarity, thus quality. The brand extension strategy, where a parent brand is extended to another product (Hennig-Thurau, Houston & Heitjans, 2009), is based on these findings. Therefore, the way studios brand their movies, enable them to often try to leverage the success of an initial movie into a sequel or successful franchise, by using brand extension (Basuroy & Chatterjee, 2008).

Balachander and Ghose (2003) and Yong, Tie-nan and Xiang-yang (2013), conceptualized the brand extension spill-over effect: The impact of franchise movies on its parent movies and sequels.

According to them, the research of Elberse and Eliashberg (2003) suggest that franchise movies have a direct spill-over effect on the success of its sequels, by directly appealing to movie goers who want to see the sequels. Balachander and Ghose (2003) named another perk of brand extension, also a direct spill-over effect, that the brand awareness of sequels is boosted because consumers already know about the franchise’s parent movie.

An indirect spill-over effect was found by Dhar, Sun and Weinberg (2012), whose research showed that franchise movies are shown in more theaters, due to anticipation of consumer demand. 2.1.3 Franchise movies and sequels performance

It appears that (at least partly) due to the spill-over effect, movies being franchise movies and/or sequels influence their own box office success: A movie being a franchise movie, generally signals that it must be of at least reasonably high quality. Most studies done on this subject verify this assumption. Chang and Ki (2005) found that a movie being a sequel is significantly related to its box office performance, indicating that sequel movies have benefits over non-sequel movies. The study performed by Moon, Bergey and Iacobucci (2010) empirically showed that sequel movies usually perform better financially, but worse in terms of ratings, than their parent movie. While in contrast, Basuroy and Chatterjee (2006) found that sequels do not match the box office revenue of its parent movie. However, they do perform better than comparable non-sequel movies, as already suggested by Chang and Ki (2005). In financial terms, Terry, Butler and De’Armond (2003) were able to

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calculate that the benefit of being a sequel garners 18 million dollar from its ‘built in audience’. Dhar, Sun and Weinberg (2012) research also showed that sequels do better than non-sequels, and

furthermore revealed that the parent movies of franchises, outdo non-franchise movies (partly due to the previously mentioned indirect spill-over effect), suggesting an overall advantage of franchise movies over non-franchise movies.

2.3 Consumer reviews and expert reviews

The reason behind people valuing consumer and expert reviews is self-evident; movies are experiential goods for which the quality is uncertain before its consumption, thus moviegoers are stimulated to look for signals which indicate quality to form their perception and movie consumption behavior (Neelamegham & Jain, 1999; Basuroy, Desai & Talukdar, 2006). Additionally, the

advancement and accessibility of the modern internet makes it possible to easily access information and both consumer and expert reviews on new movies (Kim, Park & Park, 2013). The effect of consumer and expert reviews can be positive and help increase the success of a particular movie, or negative and have the opposite effect (Liu, 2006; Craig, Greene & Versaci, 2015).

In this research the consumer and expert reviews will be divided in two factors: the amount of reviews available (frequency) and the average valence rating of all reviews (rating score). 2.1.1 Consumer reviews

Consumer reviews are a form of word-of-mouth, and nowadays consumers can easily communicate their experiences with movies all over the world. Their reviews can function as both informants and recommenders (Park, Lee & Han, 2007) and in those ways affect movie-goers choices. Many studies have already established an effect, caused by the signals of quality provided through reviews, although with some differing results.

Kim, Park and Park (2013) found that while consumer reviews overall are an important factor for commercial success, the frequency of consumer reviews mattered, while the rating score was unimportant. This is in line with the previous study done by Liu (2006), who determined that the volume of online word-of-mouth, but not the valence rating offered explanatory power for box office revenue as both an influencer and predictor. Duan, Gu and Whinston (2005) found a similar effect as well: the rating of online users had no significant effect on a movies box office

performance, but the number of online postings did significantly influence its financial success. However, the study performed by Chintagunta, Gopinath & Venkataraman (2010) did find that the average user rating had a significant effect on box office performance, while other earlier findings were found not significant (volume and variance).

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2.1.2 Expert reviews

Professional reviewers work in, and affect many product and service industries. Their reviews are especially influential in the entertainment industry (Eliashberg & Shugan, 1997), and have already been determined as a signal of quality influencing movies’ box office success various times.

Eliashberg and Shugan’s study (1997) found that expert or critic reviews correlate with the box office revenues of a movie, but that they only function as predictors and not as influencers. Basuroy, Chatterjee and Ravid (2003) did however show that critics’ reviews play a dual role: as both predictor and influencer. Furthermore, their research indicated that negative reviews hurt

performance more than positive reviews help performance, and that the impact of negative reviews seemed to diminish over time. The study done by Kim, Park & Park (2013), also established expert reviews as an important indicator of box office success. However, to the contrary of consumer reviews, only the rating score, but not the frequency was a significant predictor of success. Terry et al. (2003) were able to derive that a 10% increase in critical approval is worth 7 million dollars in revenue, underlining the effect of expert reviews.

2.1.3 Difference between consumer and expert reviews

As mentioned in the sections above, one of the recurring differences between consumer reviews and expert reviews is that for consumer reviews its frequency usually proved significant, while the rating score was insignificant. The opposite was true for expert reviews, where the rating score was established as significant and the frequency as insignificant.

This could partly be due to the fact, found by Holbrook (1999), that consumer reviewers and expert reviewers give priority to different criteria when forming their tastes in movies, and furthermore, according to Chakravarty, Liu and Mazumdar (2010), are likely to focus on different features of a movie. Expert reviewers are found to offer more independent opinions, with their attention aimed at the more technical and/or artistic aspects (Holbrook, 2005), such as the acting and directorial excellence, believability or absurdity of the story line, screenplay and movie editing (Chakravarty, Liu & Mazumdar, 2010).

Basuroy, Chatterjee and Ravid (2003) had also found the differing results between consumer and expert reviews, and suggested as a reason, that the expectations of people seeking information about movies differ between the sources they obtain it from: From experts, the rating score may be more valued as a signal of quality, while from consumers a higher volume of reviews is expected to signal quality.

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2.4 Reviews’ effect on sequels

As described in 2.1.3, franchise movies are positively affected by the spill-over effect (Yong, Tie-nan & Xiang-yang, 2013). Both direct and indirect spill-over effects boost the performance of sequels, while parent movies’ performance is improved by the indirect spill-over effect found by Dhar, Sun and Weinberg (2012). However, parent movies of franchises which were produced because of the parent movie’s success, in contrast to the parent movies of franchises which were planned, might not have been affected by the indirect spill-over effect at all. Since, at the time of its release, it was not intended as a franchise movie, thus not recognized and handled as one.

Because, as described above, the spill-over effects possibly affect sequels stronger than it affects parent movies, the signals of quality provided and sought from reviews might have a weaker effect on them; namely, reviews might have weaker explanatory power with sequels.

In short, due to the spill-over effect which affects franchises, it could be possible that reviews have a weaker effect on sequels of a franchise than on the franchise’s parent movie.

2.5 Production Budget

Higher production budgets for movies usually translate into greater production value, and should increase a movie’s quality and entertainment, because of more exotic locations, more popular stars and directors and more grand decors, sets and clothing (Litman & Ahn, 1998). The production budget is one of the most researched factors in studies done on this subject, found to be a factor positively affecting movies’ box office; partly because a high production budget often signals that the movie must be of high quality to consumers. (Basuroy et al., 2003; Chang & Ki, 2005; Terry et al. 2003, Basuroy & Chatterjee, 2008; Elberse & Eliashberg, 2003). As it might influence the relationship of the other aspects to box office revenue, it will be included as a control variable.

2.6 Box office performance

A multitude of studies, including many of the ones mentioned in this section, have already examined attributes of movies that function as driver for their financial success. The reason for this is that Hollywood studios consider the box office revenue as the most important performance measure for a movie: A movie’s success in the eyes of movie studios will largely be based upon its box office success and profit, while its critical acclaim is secondary - although it might not be for the actors and directors of the movie. Therefore, the performance measure used in this study will be the U.S. domestic box office revenue of movies.

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2.7 Hypotheses

Based on the discussion above, the following hypotheses were formulated:

H1: The positive relationship between the rating score of consumer reviews and movies’ box office revenue, is weaker for sequels than for parent movies

H2: The positive relationship between the frequency of consumer reviews and movies’ box office revenue, is weaker for sequels than for parent movies

H3: The positive relationship between the rating score of expert reviews and movies’ box office revenue, is weaker for sequels than for parent movies

H4: The positive relationship between the frequency of expert reviews and movies’ box office revenue, is weaker for sequels than for parent movies

SQ1: Between consumer reviews and expert reviews, which is a more important factor for franchise movies’ box office success?

SQ2: Between parent movies and sequels, which is more financially successful and which is more successful in terms of critical acclaim?

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3. Methodology

This section describes the research design, source of the data, the different kinds of variables, and the methods of testing.

3.1 Data source

The data used in this study is a random sample collected from multiple easily accessible online databases: the Internet Movie Database (IMDb: http://www.imdb.com), Metacritic

(http://www.metacritic.com) and Box Office Mojo (http://www.boxofficemojo.com), which all provide online movie information services. IMDb has an estimated 115.000.000 unique monthly visitors, making it an ideal source for obtaining the data on consumer reviews, both frequency and rating score. Metacritic is a site which aggregates the scores of expert reviews on movies from a number of different sources, making it a reliable place for collecting data. Box Office Mojo is the leading online box-office reporting service, and contains data on the revenues and budget of almost all feature films released in the U.S.

The data includes a total of 417 movies, released in the United States between 1972 and 2015, divided over 143 franchises, averaging roughly 3 movies per franchise (1 parent movie and 2

sequels), totaling into 274 sequels and 143 parent movies. For a high reliability, the sample contains a mixture of genres and franchises of differing financial and acclaimed success. But, all movies were released by Hollywood movie studios, which will make it applicable only for the western movie industry.

3.2 Dependent variable

This study examines only one dependent variable: the total box office revenue in the U.S. domestic market. The box office revenue is seen as a measurement for the success of a movie and is easily accessible. In the dataset used, the revenue ranges from $2,400,000 to $936,627,416.

3.3 Independent variables

Two different groups of independent variables were classified in this study: 3.3.1 Sequels

As it is theorized that the effect of reviews on sequels is weaker than on parent movies, the data set has to be split into parent movies and sequel movies. This was done by dummy coding. The dummy variable “Sequel” was created, which has the value 1 if it is a sequel in its franchise, while parent movies have the value 0.

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3.3.2 Consumer and expert reviews

The last group of included in this study are consumer reviews and expert reviews. Of both kinds of reviews two elements were examined: The frequency and the rating score. The data for both aspects of the consumer reviews were taken from IMDb, which included both U.S. and international

consumers. Although it was possible to use obtain data provided only by U.S. users from IMDb, it was chosen to use all user data. The reason behind this, is that these were the rating scores and frequency shown on each movies own webpage, thus being easier accessible for users, while specific data would matter less to them. Also, since it was not possible to find the data of consumer reviews leading up to a movie’s release and during its first week (actual consumer reviews that would matter to a movie’s box office success), the present frequency and rating score was used, functioning as an indicator for a movie’s initial consumer reviews.

The data source for expert reviews was Metacritic, of which practically all reviews were by U.S. critics. Almost all expert reviews were placed before or just after the movie’s release. For movies that were released before the emergence of the modern internet, its online expert reviews were expert reviews from its time, uploaded on the internet. For a handful of movies of which no data was available from Metacritic, it was acquired from Rotten Tomatoes (http://www.rottentomatoes.com), which is a similar aggregating website.

3.3.3 Production Budget

As the production budget has been repeatedly proved to be significant factor in the box office success of movies (Kim, Park & Park, 2013), and thus might influence the relationship between the dependent and the independent variables, it is used as a control variable in this study.

3.4 Methods

First, the descriptive statistics and inter-variable correlations will be analyzed, in search of any preliminary evidence regarding the hypotheses and sub-questions. If there are correlations found between the independent variables, the multicollinearity must also be tested.

To test the hypotheses, the actual relationships, and their strengths, between the dependent and independent variables, will be tested by conducting a linear multiple regression analysis. Any found differences will be tested for significance using an interaction term.

To explore the first sub-questions, all franchise movie data will be analyzed using a linear multiple regression. The second sub-question will be investigated by comparing the means of parent movies and sequel movies with an independent-samples t test.

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4. Results

In this chapter the results of different kinds of data analyses will be presented. First, the descriptive statistics will be shown and described, followed by a paragraph where the Pearson correlations between the variables of the data sample set will be displayed. Ending with the section of hypotheses testing, where the results of the regression analyses are outlined, evaluated and compared.

4.1 Descriptive Statistics

The descriptive statistics are explored to give some insight into the database, its variables, and their input.

4.1.1 Franchise movies

The average box office revenue of all franchise movies in the U.S. domestic market was roughly $143.6 million. The highest grossing movie was: ‘Star Wars: Episode VII - The Force Awakens’, which earned $936,627,416. The average money spent on the production budget was about $77.4 million, with ‘Pirates of the Caribbean: At World's End’ being the most expensive, costing $300 million. On average, the frequency of expert reviews was 29 with the mean rating score being out 54 of 100 (SD = 16.35). In terms of consumer reviews, the average frequency was 230.176 ratings, while the average rating score was 6.5 out of 10 (SD = 1.07). The highest rated movie by both experts and consumers was ‘The Godfather’, scoring 100 out of 100 and 9.2 out of 10 respectively.

4.1.2 Parent movies vs. sequels

The sample includes 143 parent movies and 274 sequels.

The revenue earned by parent movies amounted up to an average of $141.7 million. Their average expert rating score was 59 (SD = 15.15), while their average consumer rating score was a 6.9 (SD = .97). The sequels’ average revenue totaled $144.6 million, with their expert rating score being 51 (SD = 16.4), and the consumer rating score averaging at 6.4 (SD = 1.08).

Based to these descriptive statistics, preliminary evidence shows that parent movies seem more successful in terms of both types of critical acclaim (59 vs 51 and 6.9 vs 6.4), while, on average, sequels appear to be more financially successful ($144.6 million vs $141.7 million).

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Variable N Minimum Maximum Mean Std. Deviation Expert Rating Score 417 11 100 53.85 16.351

Expert Frequency 417 7 56 29.34 10.062 Consumer Rating Score 417 2.9 9.2 6.539 1.0732

Consumer Frequency 417 2133 1650985 230176.43 222350.724 Production Budget 412 15000 300000000 77385703.07 63601250.700 Box Office Revenue 417 2400000 936627416 143616883.58 113625079.309

Table 1: Descriptive statistics of all franchise movies

Variable N Minimum Maximum Mean Std. Deviation Expert Rating Score 143 27 100 58.62 15.151

Expert Frequency 143 7 56 28.30 9.701 Consumer Rating Score 143 4.6 9.2 6.899 0.9734

Consumer Frequency 143 9740 1222641 283341.30 249326.582 Production Budget 140 15000 230000000 56015071.43 49795274.706 Box Office Revenue 143 2400000 623279547 141699266.05 97234679.794

Table 2: Descriptive statistics of parent movies (Dummy variable sequel = 0)

Variable N Minimum Maximum Mean Std. Deviation Expert Rating Score 274 11 94 51.35 16.426

Expert Frequency 274 9 54 29.88 10.222 Consumer Rating Score 274 2.9 9.0 6.352 1.0767

Consumer Frequency 274 2133 1650985 202429.80 201864.819 Production Budget 272 2700000 300000000 88385292.89 67124264.092 Box Office Revenue 274 5820649 936627416 144617683.97 121460630.373

Table 3: Descriptive statistics of sequel movies (Dummy variable sequel = 1)

4.2 Correlations

The correlation matrixes show the inter-correlations of the variables in this study, for all franchise movies (table 4), parent movies (table 5), and sequels (table 6).

4.2.1 Franchise Movies

All review-related variables and the production budget variable, show a positive correlation with Box Office Revenue. Expert Rating Score (r = .432), Expert Frequency (r = .451) and Consumer Rating Score (r = .434) have a moderate positive correlation with Box Office Revenue, while Consumer Frequency (r = .637) and Production Budget (r = .642) show a strong positive correlation. The Dummy variable sequel is not significantly correlated with Box Office Revenue, which is unexpected according to the literature in the theoretic framework. It does however have a weak negative correlation with both Expert Rating Score (r = -.211) and Consumer Rating Score (r = -.242), which indicates preliminary evidence for parent movies being more successful in terms of critical acclaim.

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Variable 1 2 3 4 5 6 1 Expert Rating Score

2 Expert Frequency .236**

3 Consumer Rating Score .776** .196**

4 Consumer Frequency .590** .321** .712**

5 Production Budget .178** .611** .183** .361**

6 Box Office Revenue .432** .451** .434** .637** .642**

7 Dummy variable sequel -.211** .075 -.242** -.173** .241** .012

**. Correlation is significant at the 0.01 level (2-tailed).

Table 4: Correlation matrix of franchise movies

4.2.2 Parent movies

For parent movie data only, all variables display a positive correlation with Box Office Revenue. Expert Rating Score (r = .327), Expert Frequency (r = .291) and Consumer Rating Score (r = .345) show a weak positive correlation with Box Office Revenue, while Consumer Frequency (r = .607) and Production Budget (r = .651) display a strong positive correlation.

Variable 1 2 3 4 5

1 Expert Rating Score

2 Expert Frequency -.010

3 Consumer Rating Score .705** .009

4 Consumer Frequency .574** .237** .740**

5 Production Budget .005 .510** .043 .325**

6 Box Office Revenue .327** .291** .345** .607** .651**

**. Correlation is significant at the 0.01 level (2-tailed).

Table 5: Correlation matrix of parent movies (Dummy variable sequel = 0)

4.2.3 Sequel movies

For sequel movie data only, all variables show a positive correlation with Box Office Revenue. Expert Rating Score (r = .491), Expert Frequency (r = .516) and Consumer Rating Score (r = .491) show a moderate positive correlation with Box Office Revenue, while Consumer Frequency (r = .607) and Production Budget (r = .651) display a strong positive correlation.

Variable 1 2 3 4 5

1 Expert Rating Score

2 Expert Frequency .381**

3 Consumer Rating Score .790** .316**

4 Consumer Frequency .585** .407** .691**

5 Production Budget .326** .657** .331** .488**

6 Box Office Revenue .491** .516** .491** .691** .663**

**. Correlation is significant at the 0.01 level (2-tailed).

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As displayed in table 5 and 6, all review-related variables of sequels show a stronger correlation with Box Office Revenue than their parent movie counterparts. Therefore, the results of the correlation analysis can be interpreted as preliminary evidence for the opposite of the proposed hypotheses. 4.2.4 Multicollinearity

Because, as shown above, correlation among the independent variables was found, there is a chance that there may be multicollinearity problems present. Thus, the multicollinearity tests of tolerance and variance inflation factor (VIF) were conducted. As displayed in table 7, the values of tolerance (>.2) and VIF (< 5) are not in a range where remedy measures are required.

Parent movies Sequel movies

Independent variable Tolerance VIF Tolerance VIF Production Budget .648 1.543 .508 1.968 Expert Rating Score .490 2.043 .351 2.850 Expert Frequency .729 1.372 .534 1.872 Consumer Rating Score .312 3.206 .291 3.435 Consumer Frequency .347 2.886 .444 2.250

Table 7: Tolerance and VIF with the dependent variables

4.3 Hypothesis testing

To test the hypotheses and answer SQ1 and SQ2, a series of hierarchical regression analyses were conducted with the dependent variable Box Office Revenue. In the first block, the production budget variable was included, and in the second block, the consumer review and expert review variables were entered. An ANOVA showed that both the parent movie regression model (F = 42.58, p<0.001) and the sequel movie regression model (F = 89.15, p<0.001) fit the data.

4.3.1 Regression model

Franchise movies Parent movies Sequel movies Block IV R2 •∆R2 β R2 •∆R2 β R2 ∆R2 β 1 PB .642*** .651*** .663*** .413 .413*** .424 .424*** .439 .439*** 2 E-RS .163** .151 0.154* E-Fq .002 -.103 .043 C-RS -.086 -.109 -.088 C-Fq .426*** .434*** .449*** .610 .197*** .614 .190*** .626 .187***

*. Correlation is significant at the 0.05 level **. Correlation is significant at the 0.01 level ***. Correlation is significant at the 0.001 level

PB = Production Budget; E = Expert; C = Consumer; RS = Rating Score; Fq = Frequency

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As shown in table 8, the three regression models accounted for variances of the dependent variable with moderate explanatory power (R2 = .610, R2 = .614 and R2 = .626, respectively).

For the overall franchise movies’ data, the production budget variable accounted for 41.3% of total variance (F = 288.21, p<.001), and the review-related variables explained an additional 19.7% of the total variance (F = 126.87, p<.001). The model of the second block showed the variables production budget (β = .474, t = 11.71, p<.001), expert rating score (β = .163, t = 3.27, p<.005), and consumer frequency (β = .426, t = 9.02, p<.001) to be significant positive predictors.

For the parent movies’ data, the production budget variable explained 42.4% of total variance (F = 101.48, p <.001), and the review-related variables added 19% to the total variance (F = 42.58, p<.001). In the model of the second block, production budget (β = .566, t = 8.49, p<.001) and consumer frequency (β = .434, t = 4.76, p<.001) were significant positive predictors.

For the sequel movies’ data, the production budget variable accounted for 43.9% of total variance (F= 211.7, p<.001), and the review-related variables explained an additional 18.7% of the total variance (F = 89.15, p<.001). The model of the second block showed the variables production budget (β = .394, t = 7.50, p<.001), expert rating score (β = .154, t = 2.43, p<.05), and consumer frequency (β = .449, t = 7.98, p<.001) to be significant positive predictors.

4.3.2 Consumer reviews

As indicated in table 8, the consumer review rating score variable is not a significant factor in

determining box office revenue for both parent movies and sequel movies, thus H1 is not supported. To determine if there is a significant difference in the effect of consumer review frequency between parent movies and sequel movies, an interaction term between the dummy sequel variable and the consumer frequency variable must be created. The interaction variable DVS_CF was computed by multiplying the dummy sequel variable with the consumer frequency variable. By running the variable DVS_CF through a linear regression, it tests the null hypothesis H0: βCF_PM = βCF_SM (CF =

Consumer Frequency, PM = Parent Movie & SM = Sequel Movie), and indicates that they are significantly different (t(DVS_CF) = 2.79, p<.01). However, the impact of the frequency of consumer reviews on sequels is greater than the impact of the frequency of consumer reviews on parent movies (βCF_SM = .449 > βCF_PM = .434), which does not only not support H2, but would support the

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4.3.3 Expert reviews

The expert reviews’ rating score variable is only a significant factor for sequels (see table 8), but not for parent movies. This does not provide support for H3, however would provide support for the opposite statement of the proposed hypothesis. Furthermore, as the expert reviews’ frequency is a significant factor for neither parent movies of sequel movies, no support for H4 is found either. 4.3.3 Sub-questions

SQ1 explored the importance of consumer reviews relative to expert reviews in terms of box office success. A linear regression analysis on box office revenue was done, which included all independent variables. The analysis showed that the consumer review frequency (β = .424; t = 8.89, p<0.001) and expert review rating scores (β = .162, t = 3.24, p<.001), were both significant factors for box office revenue, and additionally, that the impact of consumer review frequency is greater than the impact of expert review rating scores. (βCons_Freq = .424 > βExp_Rat = .162)

SQ2 investigated the relative success of parent movies and sequels, in terms of box office revenue and critical acclaim. An independent-samples t test was performed to determine if the differences of the means of consumer rating scores, expert rating scores, and box office revenue, between parent movies and sequel movies, (see table 2 and 3) are significant.

On average, the consumer review rating score for parent movies (M = 6.9, SE = 0.081) is higher than for sequels (M = 6.4, SE = 0.065), and this difference was found significant (t = 5.08, p<.001). In addition, the expert review rating scores for parent movies (M = 59, SE = 1.27) are also higher than for sequel movies (M = 51, SE = 0.99), and the difference is also significant (t = 4.40, p<.001). As both differences in reviews’ rating scores between parent movies and sequels are significant, it can be concluded that parent movies are more successful in terms of critical acclaim, compared to sequel movies.

The average box office revenue of parent movies (M = 141.7*106, SE = 813*104) is lower than the

average box office revenue of sequels (M = 144.6*106, SE = 734*104). However, the difference is not

significant (t = -.266, p>.05), and therefore it can be concluded that there is no significant difference between the financial success of parent movies and sequels.

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5. Discussion

In the following section, the results presented previously are interpreted and discussed through the earlier-presented literature. Then the outcomes of the proposed hypotheses and sub-questions are addressed, analyzed and discussed. Followed by practical implications, limitations and suggestion for further research, and the final conclusion of this research paper.

5.1 Results

The aim of this study is to examine the effect of consumer and expert reviews on the box office revenue of Hollywood franchise movies in the U.S. domestic market, and in this way, draw some insights in the differences in their effect between parent movies and sequels. The proposed hypotheses of the research were based on the signaling theory (Kirmano & Rao,2000; Basuroy & Chatterjee, 2008). It was predicted that the positive effect on box office revenue, caused by the signaling of quality through consumer review and expert reviews, would be weaker for sequel movies than for parent movies. The reasoning behind this theory is that since sequels benefit more from the spill-over effect (Balachander & Ghose, 2003; Yong, Tie-nan & Xiang-yang, 2013), than parent movies, consumers would be less receptive to the signals of quality delivered and sought through consumer and expert reviews.

The results demonstrate findings of which most are in line with earlier studies. The production budget was determined to be a significant factor positively influencing box office revenue for franchise movies overall, parent movies and sequels, in agreement with prior studies (Basuroy, Chatterjee & Ravid, 2003; Chang & Ki, 2005; Terry et al. 2003, Basuroy & Chatterjee, 2008; Elberse & Eliashberg, 2003). As also found in the studies done by Duan, Gu and Whinston (2005), Liu (2006), and Kim, Park and Park (2013), it was found that with consumer reviews, the amount of reviews matter, while the rating score did not. For expert reviews, the results were also mostly in line with earlier studies which showed the expert rating score to be significant, but not the frequency (Basuroy et al., 2003; Kim, Park & Park, 2013). However, in this study, this effect was found significant for franchise movies overall and sequel movies, but not for parent movies.

5.2 Tested Hypotheses

The purpose of the hypotheses was to confirm a difference in effect of reviews between parent movies and sequels, for both review rating score and frequency, where the effect would be weaker for sequel movies.

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5.2.1 Hypotheses

Hypothesis 1 focused on determining the weaker effect of consumer review rating score on box office revenue for sequels. As mentioned before, the consumer rating score was not found as a significant factor influencing box office revenue. Thus, because of the assumption in hypothesis 1, that the consumer rating score would be significant, is found not true, it would be impossible to find support for it. This result could have been expected, as the rating score of consumer reviews has already been determined as non-significant in multiple prior studies on the subject.

The function of hypothesis 2 was to determine the effect of consumer review frequency on box office revenue being weaker for sequel movies. The assumption of the positive relationship in the hypothesis was confirmed, but not only was there no support for H2, the data showed support for the reverse statement of the hypothesis. The effect of consumer review frequency was found to be significantly stronger for sequels than for parent movies.

Hypothesis 3 aimed to show that the effect of expert rating scores on box office revenue would be weaker for sequels. The analysis found this effect to be significant for sequels, but not for parent movies. Indicating support for the effect being stronger for sequels, which did not support H3, as it is evidence for the opposite. This result was in line with the findings for H2, where the effect of

consumer reviews frequency was also found stronger for parent movies, instead of the contrary. The objective for hypothesis 4 was to determine that the effect of expert review frequency on box office revenue is weaker for sequels. As could have been expected, no support for hypothesis 4 was found either. The assumption in the hypothesis, the positive effect of expert review frequency on box office revenue, was not found significant, making it not possible to any difference in effect between parent movies and sequels.

It could have been expected that H1 and H2 would find no support, as the assumptions in both hypotheses have already been found not significant in multiple prior studies (Basuroy, Chatterjee & Ravid, 2003; Duan, Gu & Whinston, 2005; Liu, 2006; Kim, Park & Park, 2013). This could be, as suggested by Basuroy et al. (2003), because the expectations of information about movies, sought by consumers, differ between the sources they seek it from: From experts, the rating score may be more valued as a signal of quality, while from consumers a higher volume of reviews is expected to signal quality. In the case of consumer review rating score, this may be explained because internet user’s movie consumption decisions are not very serious decision to be made. Seeing a movie in the theater is a relatively cheap entertainment activity, so the amount of reviews of consumers could play an integral role (Kim, Park & Park, 2013). Furthermore, as consumer reviews are often seen less

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objective and dependent on a consumers taste (Holbrook, 1999), their rating scores can be seen as less valuable, thus not influencing movie consumption behavior.

The results for H3 and H4 were however unexpected. The research did not only show no support for the hypothesis, but also determined that the, significant factors consumer review frequency and expert review rating score had a stronger effect, or were a significant factor, for the box office revenue of sequels, while having a weaker effect, of an insignificant factor, for that of parent movies. This was the exact opposite of the proposed expectations. A possible explanation could be that due to the fact that sequels are often regarded as being of less quality than the parent movie (Moon, Bergey & Iacobucci, 2010), consumers are stimulated to seek signals about a sequel movie’s quality through reviews, and in this way increase their effect. Further theorizing and research will be needed to confirm this or other theories regarding the subject of why reviews’ effect is stronger/significant for sequel movies, and weaker/not significant for parent movies. 5.2.2 Sub-question

Exploration of SQ1 indicated that, for franchise movies, the consumer review frequency is a more important factor for box office revenue than the expert review rating score, which is in line with the earlier findings of Kim, Park & Park (2013).

Out of the investigation of SQ2, it could be determined that there was no significant difference between the average box office revenue of parent movies and sequels (although the average revenue was relatively slightly higher for sequels). This result was not found in earlier studies. However, as all parent movies were grouped together and all sequels were grouped together, this result could indicate only a not-significant difference for franchise movies overall, while the differences within franchises could be divergent.

In terms of critical acclaim from both expert reviews and consumer reviews, parent movies were determined as significantly more successful. This is in line with the study done by Moon, Bergey and Iacobucci (2010). This could be due to the fact that creative and unexpected experiential goods are viewed as of higher entertainment value (Neelamegham & Jain, 1999); The plots and ideas of parent movies can often be new and surprising, while sequels usually repeat some of the plots and ideas found in their parent movie (Sood & Drèze, 2006), resulting in them possibly being regarded as of lesser entertainment value, and thus lower ratings.

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5.3 Managerial Implications

As movie production and marketing costs keep rising, it is crucial for movie studios to find, improve and manage factors which enhance box office revenue. Many of these factors can be considered as signals of quality which can be sent both intentionally and unintentionally by movie studios.

This research has displayed that signals of quality through consumer reviews and expert reviews are of significant importance in respect to the box office success of franchise movies. Thus, the movie industry should be aware of the fact that garnering favorable ratings from expert reviewers, and creating buzz to increase the amount of consumer reviews, will enhance the financial success of their movies.

Although the proposed hypotheses of this study were not supported, a possible stronger/significant effect of reviews on the box office success of sequels was found. Further research is needed to explore these outcomes. If these results are confirmed, movie studios should utilize these findings to develop their branding and marketing strategy for sequels accordingly.

During the exploration of the sub-questions in this study, it was determined that although sequels, on average, are less successful in terms of critical acclaim, their box office revenues are on average, similar to those of the parent movies. Two insights can be drawn from these findings: First, as sequels are on average found to be as financially successful as parent movies, pursuing successful franchises, and leveraging successful movies into franchises, are found to be valuable strategies. However, since the average production budget of sequels ($88.4 million: see table 8) is considerably higher than the budget for parent movies ($56 million), this could possibly harm the profitability of sequel movies, if the budget is not managed properly.

5.4 Limitations and future research

This research suffers from a few limitations which will be described in this paragraph. These limitations should be reported and could be addressed during future research.

First of all, while some movies and their data in the sample are several decades old, inflation was not taken into account during the analysis of box office revenue. This could lead to biased results, as it does not accurately reflect the relative financial success. A solution for this limitation could be using theater attendance figures as a dependent variable, instead of box office revenue. However, this data might be more difficult to attain.

Furthermore, the data on consumer reviews was collected as the current volume and rating score of consumer reviews from IMDB. This was because the preferred data of the amount and rating of pre-release and during the first week reviews, are not publicly accessible. The data does however,

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during the pre-release period and the first week would be preferred. Another limitation connected to this issue, is that popular movies might have received more and more favorable ratings since their release because of their popularity. Underlining the importance of more accurate consumer review data. In addition, as many of the movies in the sample were released before the emergence of modern internet, their online consumer rating score and frequency were determined later. It does however function as an indicator for the reviews during the time of the movie’s release, but suffers from the same popularity bias as mentioned above. The online volume and rating score of expert reviews are however generally uploaded reviews of during the movie’s release period.

Finally, as the consumer review frequency could be biased due to popularity, the relative importance between consumer reviews and expert reviews could be inaccurate.

In addition to the suggestions for future research which address the above limitations, a researched focused on theorizing and investigating why there seems to be evidence for a stronger effect of reviews on the box office revenue of sequels than for parent movies is suggested.

5.5 Conclusion

This research was conducted in an attempt to draw some insights into the differences of the effects of both consumer reviews and expert reviews on box office revenue, between franchise parent movies and sequels. The study contributes to the existing literature by investigating this difference, which was unprecedented. A proposition was formed, which theorized that because of the spill-over effect influencing franchise movies, and in particular sequels, the positive relationship between reviews and box office revenue would be weaker for parent movies than for sequels. Most results were in line with earlier findings. But, the hypotheses formulated according to the above assumption were not supported, indicating the theorization must be incorrect. Furthermore, the sub-questions investigated during this research showed consumer reviews to be a more important factor for the box office success. They also determined parent movies to be more successful in terms of critical acclaim, while no significant difference in financial success was found between the two.

Although no support was found for the hypotheses, evidence for a possible contrasting view was found. The effect of reviews seems to be stronger/significant for sequels, instead of parent movies. An explanation may lie in the experiential nature of movies, but further research will be necessary to confirm these findings.

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6.

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