The hidden potential of star power and movie awards in the motion picture industry : a study of signaling theory, word-of-mouth, and consumer reviews as mediators for the effect of star power on box-office performance

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The hidden Potential of Star Power

and Movie Awards in the Motion

Picture Industry

A study of Signaling Theory, Word-‐of-‐Mouth, and Consumer Reviews as

mediators for the effect of Star Power on Box-‐Office Performance

Chaiyakit Limsuval

10435689

Bachelor Thesis

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

Supervisor:

Dr. Frederik B. Situmeang

June 27th_{,
2015}

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ABSTRACT

Despite the importance of quality signals in the creative industries, the

relationships of different types of signals have not been explored

extensively yet. To analyze the extent of influence that star power and

consumer reviews have on box-office performance, current literature on

the concept of signaling theory was assessed. This paper proposed that

consumer reviews and star power positively affects movie revenues.

Further it is hypothesized that consumer reviews mediate the effects of

star power on movie revenues. In addition, this research paper extends on

previous research by categorizing star power in two subcategories: award

winners and nominees. These propositions were tested through data

derived from online movie review platforms. The findings reveal that

only award winners and review volume have a direct effect on movie

revenues. Award nominees only have an indirect effect on box-office

performance through their influence on review volume. This result may

shed more light on the underlying concept of WOM for future research

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Statement of Originality

This document is written by the student Chaiyakit Limsuval, 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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1. Introduction

“Furious 7” the latest movie of the “Fast and Furious” franchise was a critically acclaimed as well as a commercial success: In the opening week the film grossed $397 million, and in total $1.489 billion worldwide, marking it the 2nd

highest-opening and the 4th highest-grossing film of all time (boxofficemojo.com). Its success

may be attributed to different factors: its star-filled cast, or the substantial buzz created in the media prior to the launch of the movie, among which was also the tragic topic of Paul Walker’s, the leading actor’s, death. In addition to being successful financially, the movie scored overall very well on multiple movie-review platforms, such as “IMDB” and “Rotten Tomatoes”.

These above-mentioned factors are quality signals, which a consumer may use to deduct information on a product in order to reduce uncertainty (Kirmani & Rao, 2000; Connelly, Certo, Ireland, & Reutzel, 2011). In the motion-picture industry, where the audience cannot probe a movie before paying for it, individuals may regard the actors who are starring in the movie as signals to evaluate the film. Having a star in the movie cast gives a signal to the audience about superior quality of the movie (Ravid, 1999), and a moviegoer who enjoyed the performance of an actor in one movie may be inclined to watch him or her starring in another (Densai & Basuroy, 2005).

Apart from star power, also product reviews serve as quality signal for the consumer. Due to various online platforms, consumers possess a convenient channel to voice their opinion, leave customer reviews, or gather information about a product. Other potential customers can use this information to assess the product in order to derive a purchase decision. Studies suggest that many consumers make offline decisions based on online information (Godes & Mayzlin, 2004). This is also reflected by findings of a study conducted by Deloitte, that 8 out of 10 individuals are influenced by user reviews (Deloitte, 2007). Thus, these effects can have an influence on the financial performance of products and services (Gupta & Harris, 2010). This implies that the effects of consumer reviews, which are part of online electronic Word-of-Mouth, should be thoroughly understood by managers, since online consumer reviews are a frequently accessed information source, which gives firms and managers the valuable opportunity to gain feedback about its surroundings (Ludwig, de Ruyter, Friedman, Brüggen, Wetzels, & Pfann, 2013; Godes & Mayzlin,

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2004). Naturally, these conditions also apply for the motion picture industry, where movie reviews act as important quality signals to potential viewers, which may determine the success or failure of a movie (Liu Y. , 2006; Elberse, 2007; Duan, Gu, & Whinston, 2008a; 2008b). These effects of the two quality signals will be analyzed in this research paper.

Previous research has investigated the influence of star power and consumer reviews on box-office performance, and found that both factors have an effect on movie revenues (Liu Y. , 2006; Elberse, 2007; Duan, Gu, & Whinston, 2008a; 2008b). However, these effects were mainly analyzed separately. Thus the authors failed to address the interconnection of these two variables and the relationship they share, which is rooted in their ability to create WOM and buzz. Further, previous research used accumulated award winnings and nominations as proxies for star power, which neglects a possible difference in the effects of those variables. This research paper aims to close these research gaps through the following research question:

“To what extent do customer reviews mediate the effects of star power on box-office performance of movies?”

In order to answer this proposed research question, a multiple-regression analysis is performed in this empirical research. Data on consumer reviews and financial box-office performance are gathered from “RottenTomatoes.com” and “The-numbers.com”. Further, Golden Globes and Academy Awards (and nominations) of

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2. Literature Review

In order to answer the earlier proposed research question, a thorough literature review will be presented in this section that will outline the underlying concepts and premises of the variables star power and customer reviews. Both variables have their foundation in signaling theory as well as WOM and buzz, which properties and effects will be outlined in the following subsections. Elaborating signaling theory, WOM, and buzz before establishing the research hypotheses will facilitate the understanding of how star power and customer reviews act as quality signals, and their effects on box-office performance.

2.1 Signaling Theory

Individuals have the tendency to be risk and loss averse, which is an issue consumers often face in the market place (Liu, Liu, & Mazumdar, 2014). The interaction and decision-making process of two parties that have asymmetric information is described by signaling theory (Connelly, Certo, Ireland, & Reutzel, 2011). In general, the first party – or the sender – chooses if, what, and how to communicate information. The second party – the receiver – needs to choose how to interpret these emitted signals (Connelly, Certo, Ireland, & Reutzel, 2011). Through this interpretation process, the second party can deduct some implicit information about the company, the goods, or services, in order to reduce information asymmetry.

In an online e-commerce setting as well as in the creative industries, the sampling of a product or service prior to the purchase is restricted or impossible. This is also supported by the fact that the provided products in the motion-picture industry are intangible for the end-user (Karniouchina, 2011). Tickets are sold in advance, movies are viewed or consumed on the spot, and usually there is no warranty for the customer. Due to this characteristic in the movie industry, an asymmetric information distribution between the film studios and the audiences prevails (Eliashberg & Sawhney, 1994). Consequently, consumers will pay close attention to signals to make inferences about the quality of a product prior to consumption (Kirmani & Rao, 2000; Gemser, Van Oostrum, & Leenders, 2007; Duan, Gu, & Whinston, 2008a). Therefore, the signals provided by the film studios play a crucial role for the success of a movie.

Whether intentionally or not, studios send quality signals through various elements, such as stars and directors that are involved in a movie. These information

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cues are publically visible and free to be interpreted by the audience. Apart from signals provided by the company itself, or in this case film studio, consumers can also gather signals from external sources, such as other moviegoers. The person who seeks information may find these through either direct interaction, or in written or online reviews. Viewers can gauge the quality of a movie by the given feedback on various movie critic platforms, such as “IMDB”, “Rotten Tomatoes”, and “Meta Critic”.

In conclusion, due to the experiential nature of movies, viewers often listen to other consumers’ opinion when deciding whether to view a movie in the theater or not (Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011).

2.2. Word-‐of-‐Mouth (WOM)

Word-of-Mouth (“WOM”), which is the informal passing of information from one individual to another, is known to be one of the most influential means of transmitting information (Dichter, 1966; Godes & Mayzlin, 2004; Liu Y. , 2006; Gupta & Harris, 2010; Lang & Lawson, 2013). The extent to which WOM becomes an effective source of information is dependent on its impact and reach (Lang & Lawson, 2013). WOM differs from other information sources in two important points: its perceived credibility and accessibility (Liu Y. , 2006).

In a social setting, the voices of individuals are more trustworthy and effective than for instance promotional efforts by organizations (Liu Y. , 2006). According to Ludwig et al. (2013), one reason for this superior level of credibility is partly rooted in the communication accommodation theory (“CAT”), which states, that greater similarities in communication styles (for instance, the speaker’s voice, gestures, and posture) will decrease the perceived social distance between the conversation participants. Further, due to the increased perception of a common social identity, the synchronization of communication styles will lead to more approval and trust (Ludwig, de Ruyter, Friedman, Brüggen, Wetzels, & Pfann, 2013).

Nowadays through the Internet and its fast pace, the speed and reach of WOM has been dramatically increased (Liu Y. , 2006), making more and richer information about products and services become more accessible. This evolution of WOM where consumers share their view over the Internet can be referred to as ‘e-WOM’ (Kietzmann & Canhoto, 2013).

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In this online setting, individuals will also use the above presented heuristics to assess the credibility and trustworthiness of others. Although these heuristics have their origin in traditional (offline) human communication theory, Ludwig et al. (2013) showed that the effects of these heuristic tools, such as ‘linguistic style match’ and ‘affective content’, also apply in the online context. The authors demonstrated that when an online review has a similar linguistic and conversational style to one’s own, the review would gain more rapport and credibility towards the reader (Ludwig, de Ruyter, Friedman, Brüggen, Wetzels, & Pfann, 2013). Further findings on conversion rate analyses by Ludwig et al. (2013) and Chevalier and Mayzlin (2006) demonstrate that negative ‘affective content’ in reviews have a higher impact on customers’ buying decisions than positive reviews. In other words, negative notions associated with a product will likely deter a customer from his or her buying intention, which stresses the importance of maintaining positive WOM.

From these findings it is evident that the Internet has a substantial influence on generating WOM, since the characteristics for effective e-WOM show high resemblance to traditional WOM theory. However, through the Internet’s immense speed, reach, and impact, its effect is additionally enhanced, which can lead e-WOM to transcend into a heightened form of WOM: a so-called “buzz”.

2.3. Marketing Buzz

Buzz is a term in marketing which describes the interaction of consumers of a product or service, which magnifies the original message (Thomas, 2004). Commonly known as ‘hype’ among consumers, this amplified form of WOM is usually triggered through heightened emotions, excitement or anticipation for a product or service (Dye, 2000).

Although originating from traditional oral WOM, the speed and reach of social media platforms, such as Facebook and Twitter, and their ability to rapidly and globally spread e-WOM has established the Internet as today’s main communication channel for buzz. A prime example for this phenomenon is the “ALS Ice Bucket Challenge”, which animated millions of people to upload videos of themselves, pouring a bucket of water over their heads. With the help of numerous participating celebrities, 28 million people have either uploaded or commented on ice-bucket-associated posts (Townsend, 2014). With this campaign, awareness and money for the research and treatment of the ALS disease has been raised, with a total of $98.2 million donations compared to $2.7 million in the previous year (Townsend, 2014).

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These presented phenomena also apply to the motion-picture industry, as can be seen in the case of the horror movie “Paranormal Activity”. Through limited screening, the extensive usage of social media channels, and other buzz marketing strategies, the movie grossed $19.6 million in the opening weekend, despite having a very small budget of only $11,000 (France, 2009; boxofficemojo.com, Paranormal Activity). This is in line with Sefert et al (2009), which additionally supports the importance of buzz marketing, by suggesting that the combined effects of advertisement and interpersonal communication have a great power in influencing consumers’ purchase decisions.

These two presented cases are examples for the enormous influence of WOM and buzz. Managers and marketers, therefore, need to pay close attention to market buzz, since it can be a crucial factor for the success of a product or campaign (Berger & Chen, 2014).

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3. Conceptual Framework

In the previous section it was shown how quality signals can influence the consumer and their buying decisions, and how WOM and buzz associated with a movie can be critical to the success of a film. Nevertheless, the consumers in the movie industry can further rely on two additional signals for estimating quality: Star power and consumer reviews, which is a written form of WOM. This section demonstrates how these variables affect box-office revenue and establish the corresponding hypotheses that will be tested to answer the proposed research question.

3.1 Star Power

Movies with high-profile star participation are said to have strong star power (Elberse, 2007). Actors, as well as directors, can qualify as stars for several reasons, but most prominently due to their critically acclaimed skills, or personality traits that appeal to the audience (Elberse, 2007).

Reasons and benefits for incorporating stars vary from stakeholder to stakeholder. In the movie industry, these stakeholders can be typically classified into four groups: Financers who provide the funds for the movie production, exhibitors who screen the movies in theaters, the news who report about the movies, and the audience who ultimately watch and pay for the movies (Liu, Liu, & Mazumdar, 2014). Although all of them contribute to a film’s success, Liu et al. (2014) find that the choice of stars are most relevant to financers, since they provide the biggest financial stake. Because demand for a movie is uncertain in the production stage, investors rely on star power as quality signals (Ravid, 1999; Liu, Liu, & Mazumdar, 2014). Having a star, who reduces risk, the investor will likely provide more funds for the production, that may improve the movie quality and promotion budget, which ultimately benefits the audience and box-office performance (Liu, Liu, & Mazumdar, 2014). This is in line with the findings of DeVany and Walls (1999), which show that movies with stars have overall higher budgets.

As mentioned earlier, also the consumer or the audience relies on quality signals when making purchase decisions. Although star power, as a signal, is intangible in nature and difficult to measure, star power and the skill level of an actor or director can be approximated through the number of received, and nominated, awards. These awards can be bestowed upon by peers (e.g. Academy Award or

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“Oscar”), experts (e.g. Golden Globes), or the consumers themselves (e.g. People’s Choice Awards & MTV Video Awards).

The participation of a star may give a signal to the audience about a superior quality of the movie (Ravid, 1999). Indeed, findings show that award winnings and nominations are correlated to movie revenues (Ravid, 1999). Further findings suggest, that a star’s historical box office performance, as well as his or her recognition – through awards – has an impact on a movie’s financial performance (Elberse, 2007). Additionally, research suggest, that in the mainstream-movie segment, the exact type of award is not relevant, since all types (e.g. Academy Award vs. MTV Video Award) are perceived as equally credible cues to consumers (Gemser, Leenders, & Wijnberg, 2008).

This recognition or familiarity with an actor or director plays a crucial role in the signal effect of stars: Stars can act as brands, that consumers can be attracted to, which may partly explain that movies with high star participation tend to be more successful than movies with unknown actors or directors (Densai & Basuroy, 2005). It is reasonable to believe that a viewer will likely go watch a movie that features a favorable star that he or she knows from past movies. Findings by Densai and Basuroy (2005), even suggest, that the sole involvement of a familiar star or director can provide enough information for the viewer to make a decision on watching a movie or not.

Lastly, this research analyzes the variable star power differently from previous research. Several research papers, including Elberse (2007) and Liu et al (2014), measure star power in terms of total award winnings and nominations. However, the power of which these conditions have an effect on the other variables, such as box-office performance, may be different between award winners and nominees. Winnings may qualify as stronger signals for quality and excellence than a mere award nomination. These possible different effects will be addressed and analyzed in this research paper.

In conclusion, movies that feature high star power should lead to superior box-office performance. Since awards and award nominations of a starring actor or director are proxies for the movies star power, it is expected that the following hypotheses hold true:

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H1a: Movies featuring stars/directors who have won awards yield higher box-office performance

H1b: Movies featuring stars/directors who were nominated for awards yield higher box-office performance

3.2 Consumer Reviews

As briefly outlined in the literature review section, one type of signal are reviews, which users can read on a company’s websites, in various media, or online platforms and forums. Reviews are informational cues given from one individual to another; therefore they can be classified as an essential part of WOM, or e-WOM in the online context.

Since viewers are exposed to uncertainty when deciding on going to a movie, they often listen to other consumers’ opinion when deciding whether to view a movie in the theater (Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011). Potential viewers can gauge the quality of a movie by the feedback given in media or can read the opinion of other viewers on various movie critic platforms, such as “IMDB”, “Rotten Tomatoes” or “Meta Critic”. A study further suggests, that about one-third of moviegoers in America decide for a film which received positive reviews (Basuroy, Chatterjee, & Ravid, 2003).

The reason why these reviews play a crucial role in consumer decisions lies in the perceived objectivity of the reviewers: third party reviewers have a bigger persuasive effect (Chang & Ki, 2005), because they do not have any stakes in the movie. Reviews can be either written by movie experts or by a regular movie visitor. Professional movie critics can take two different roles with their reviews: they can be ‘influencers’ or ‘predictors’ (Eliashberg & Shugan, 1997 ).

In the first role, the expert acts as an opinion leader due to his/her perception of having extensive knowledge about movies, who may influence the audience to see a movie or not (Basuroy, Chatterjee, & Ravid, 2003; Eliashberg & Shugan, 1997 ). Consequently, as influencers, the movie’s success – especially in the opening week – is dependent on their judgment (Eliashberg & Shugan, 1997 ). This may lay in the fact that critics gain early access to movies prior to the releases, making their reviews the only publicly available information cue for the quality of the movie (Basuroy, Chatterjee, & Ravid, 2003). In the second role, movie experts are seen as predictors,

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who merely forecast if the film will be liked by the audience or not (Eliashberg & Shugan, 1997 ).

Studies show that critics’ reviews have no significant influence on early box-office performance, but correlate with late box-office performance (Eliashberg & Shugan, 1997 ), which suggest that expert reviews have a predicting role instead of an influencing one (Chang & Ki, 2005).

Since the predicting aspect of critics reviews seems to relate to the movie’s success, it is reasonable for research to pay attention to the source of prediction itself: the viewers’ taste. Because critics in the role of predictors estimate the consumers’ preferences, it makes sense to analyze the consumer reviews directly, which is simple nowadays due to platforms such as “Rotten Tomatoes”. This may also avoid the widely believed criticism, that critics do not truly reflect the general viewers’ taste, since professional critics have more expertise and may put emphasis on different criteria when developing a review (Prag & Casavant, 1994; Basuroy, Chatterjee, & Ravid, 2003; Moon, Bergey, & Iacobucci, 2010). However, findings by Wanderer (1987, in Eliashberg & Shugan, 1997) show that reviews by expert critics are similar to the consumers’ opinion, which is in line with previously presented findings on the signal power of different awards (expert/peer vs. consumer chosen award ceremonies) (Gemser, Leenders, & Wijnberg, 2008).

Nevertheless this research will focus on the consumer reviews, since popular online review platforms, such as “IMDB” and “Rotten Tomatoes”, are frequently accessed information sources that have user-generated content. Apart from this, consumer reviews will be analyzed in this paper, due to its role as a significant source and driver of WOM, which effects can be responsible for the success or failure of a movie (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a).

Since consumer reviews are a type of WOM, one can infer from the arguments presented in the literature review, that these reviews have an influence on the financial performance of a movie. However, contrary to simple intuition, multiple findings cannot establish a direct effect of qualitative valence of reviews on box-office revenues (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a). The reason for this is that the effects of consumer reviews unfold through their volume and the generated WOM or buzz (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a; 2008b; Karniouchina, 2011).

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Duan, Gu and Whinston (2008a) analyzed movie reviews as being endogenous in nature, and revealed that consumer reviews and movie sales are influencing each other. In other words, viewers who have seen a movie write reviews, which in return cause more viewers to see the movie. This finding suggests, that the increased awareness of a movie positively influences box-office performance (Duan, Gu, & Whinston, 2008a).

Nevertheless, the valence of reviews does have an indirect effect on revenues: Duan, Gu and Whinston (2008b) expanded on the previous findings and showed that valance have an influence on the volume of reviews. Though there is no direct effect, this means the qualitative valence of reviews influences box-office performance indirectly by generating higher volumes of reviews (Duan, Gu, & Whinston, 2008b). In other words, the more positive the valence of WOM associated with a certain movie, the more motivation these consumers have to spread the word about their experience, and therefore generate more WOM (Duan, Gu, & Whinston, 2008b).

In sum, through positive valence, the increased volume and its underlying WOM and awareness effect have a positive influence on movie revenues (Duan, Gu, & Whinston, 2008a; 2008b). Accounting for these arguments, the hypothesis which will be analyzed are as following:

H2a: Valance of consumer reviews influences volume of consumer reviews

H2b: High volume of consumer reviews yields higher box office performance

3.3 Interaction of Consumer Reviews with Star Power

The previously presented studies and arguments show that WOM or review valence indirectly increases financial performance, through the generation of higher volume of WOM and buzz (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a; 2008b; Karniouchina, 2011). This research paper will extend on these findings by establishing and investigating the relationship of star power with consumer reviews and WOM.

The common denominator which star power and consumer reviews have, are

rooted in their ability to create WOM and buzz, which positively correlates to product

sales (Luo & Zhang, 2013). Apart from being quality signals in terms of artistic performance, stars (and directors) and their star power also entail the ability of generating free promotion through WOM and buzz creation (Duan, Gu, & Whinston,

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2008b; Karniouchina, 2011; Liu, Liu, & Mazumdar, 2014). High-profile stars are likely to capture the public’s attention and provide indirect advertisement for the movie, which may enhance box-office performance (Liu, Liu, & Mazumdar, 2014). This is in line with the findings of Chakravarty et al. (2010) and Karniouchina (2011), that highlight the importance of Star-generated buzz during the launch, or the first weeks of a movie (De Vany & Walls, 1999).

This phenomenon magnifies the importance of star power in movies, since the high profile “celebrity aura” of stars and directors should increase the volume of WOM and buzz associated with the movie. This heightened awareness is found to positively increase box-office performance, as well as spawning more online reviews, which further enhances WOM and buzz. (Duan, Gu, & Whinston, 2008b). This endogenous cycle of consumer reviews should lead to higher box-office performance (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a; 2008b; Karniouchina, 2011; Luo & Zhang, 2013).

Apart from creating higher volume, it is reasonable to assume that star power, which is measured by the quantity of awards and nominations, is a predictor of review valence. This is due to the fact, that awards are a proxy for excellence and quality, and the judgments by jury is a reflection of the audiences’ taste (Eliashberg & Shugan, 1997 ; Gemser, Leenders, & Wijnberg, 2008). Further, although Duan, Gu and Whinston (2008a; 2008b) suggest that only review volume is the factor, that directly influences box-office performance, the star power variables may have an effect on review valence, which in turn affects volume. This suspected indirect effect advocates for the inclusion of review valance in this framework.

In conclusion, consumer reviews may act as a channel through which stars and directors unfold their WOM and buzz generating ability. Due to this, the independent variable consumer review would act as a mediator, which may mediate the effects of star power on box-office performance. From this line of reasoning, the following hypotheses can be constructed:

H3a: Awarded stars in a movie generate higher valence of online reviews

H3b: Award-nominated stars generate higher valence of online reviews

H4a: Awarded stars in a movie generate higher volume of online reviews

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Mediations

H5a: Review valence mediates the effects of awarded stars on movie revenues

H5a: Review valence mediates the effects of award-nominated stars on movie revenues

H6b: Review volume mediates the effects of awarded stars on movie revenues

H6b: Review volume mediates the effects of award-nominated stars on movie revenues

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3.4 Conceptual Model

The following constructed conceptual model is a visualization of the previously proposed framework and hypotheses:

H1a: Movies featuring stars/directors who have won awards yield higher movie revenues

H1b: Movies featuring stars/directors who were nominated for awards yield higher movie revenues

H2a: Valance of consumer reviews influences volume of consumer reviews

H2b: High volume of consumer reviews yields higher box office performance

H3a: Awarded stars in a movie generate higher valence of online reviews

H3b: Award-nominated stars generate higher valence of online reviews

H4a: Awarded stars in a movie generate higher volume of online reviews

H4b: Award-nominated stars generate higher volume of online reviews

H5a: Review valence mediates the effects of stars with awards on movie revenues

H5a: Review valence mediates the effects of stars with award nominations on movie revenues

H6b: Review volume mediates the effects of stars with awards on movie revenues

H6b: Review volume mediates the effects of stars with award nominations on movie

Star Power

Movie Revenues

Consumer Reviews

H5 & H6 (Mediated Effect)

Valance Volume Winners Nominees H1a H1b H2a H2b H3a H4a H3b H4b

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4. Methodology

Having outlined the conceptual framework and proposed the hypotheses that will be tested, this section will elaborate the research design, measurements of the variables, the sample characteristics, and the method of testing.

The hypotheses of this research will be examined through a cross-sectional multiple regression analysis. This research will analyze the U.S. motion picture industry since it is the largest cultural and economical contributor to the global movie industry (Moon, Bergey, & Iacobucci, 2010), and will therefore yield sufficient amount of data suitable for this research. Secondary quantitative data, which is publically and globally available, will be used. Due to this it can be assumed that the quality of the gathered data is reliable (Saunders, Lewis, & Thornhil, 2011).

Data Collection and Sample Characteristics

Box-office performance and movie revenues of various motion pictures, that were featured in the last years, are gathered from the publically available online database of “The-Numbers.com”. For this cross-sectional research, the total grossed box-office revenues are used in this paper.

The magnitude of an actor’s star power is derived from received awards and nominations, which can be used as proxies to determine an actor’s quality. In this research the focus will be on US based award associations, given that the US motion-picture industry is the biggest and most significant one worldwide. Further, awards that will be incorporated are given out by a peer- and expert-selection. Consumer-based awards are neglected in this research, since expert reviews are sufficient proxy for the consumer’s taste, and findings suggest that these are equal quality cues to the mainstream-audience (Gemser, Leenders, & Wijnberg, 2008). For peer-selected awards, the winners and nominees of the Academy Awards (also known as “The Oscars”) will be analyzed, in the categories “Best Actor in a Leading Role”, “Best Actor in a Supporting Role”, as well as their female counter parts, and “Best Directing”. For expert-selected awards, the winners and nominees of the “Golden Globe Award” will be assessed, in similar categories as The Oscars. The difference arises in the category “Best Actor in a Leading Role” and “Best Actress in a Leading Role” since the Golden Globes specify these categories into two genres: Drama and

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Musical/Comedy. In this research both categories will be incorporated, yielding (at least) two “Leading Actors” and two “Leading Actresses” per year. The timeframe of this research are the ceremonies held between 1980 and 2014.

One should note that although the ceremonies reward the achievement in movies of the corresponding previous years, it is reasonable to assume that the signalizing effect begins at the point of the award reception/nomination, which is in the beginning of the subsequent year (typically in January). Additionally in line with that, the awareness-effect truly unfolds after the public broadcast, since the worldwide reach of these shows spreads and magnifies the generated WOM and buzz.

Lastly, the reason for incorporating the Academy Awards and the Golden Globes are their prestigious reputation, as well as the earlier mentioned power to generate WOM through their worldwide airing.

Consumer review valence is derived from quantitative rating scores, which are obtained from “Rottentomatoes.com”. It provides user scores on a scale of 0 to 10, where a score of 7 and higher is regarded as a positive review by the website. Volume can be deducted through the total number of reviews left.

In addition, this study includes two control variables: expert scores and the production budget of a movie. As explained in the theoretical framework, expert scores can act as influencers as well as predictors, which might affect the box-office performance of a movie (Basuroy, Chatterjee, & Ravid, 2003; Eliashberg & Shugan, 1997 ). These data are also available from “Rottentomatoes.com”. The production budget of a movie is included, since findings have shown that movies with high profile stars tend to also have a higher budget (De Vany & Walls, 1999), which may influence the reviews given, through the increased advertisement and buzz generation. This information is obtainable through “The-Numbers.com”.

Models and Method

To test the proposed hypotheses, a liner regression analysis is performed using SPSS as well as the add-on software “AMOS”, where pathway analysis corresponds to the earlier outlined conceptual framework. To test for mediation, first the main effects of the independent variables are analyzed and tested for significance. This will then be repeated including the mediation variable consumer reviews.

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In this research, the independent variable star power will be tested differently from preceding research: As already mentioned in the theoretical framework, contrary to previous research such as Elberse (2007) and Liu et al (2014), where a star’s artistic performance was measured by the sum of award winnings and nominations, this research will analyze these variables separately. By doing so, this research will also obtain a more detailed picture on the characteristics of star power, since winners and nominees might have different implications: One of these variables might have a stronger or weaker effect, which would shed more light on the award ceremonies as quality signals. Therefore, this construct may yield more detailed information and expand on the findings of the previous research.

These modified variables of star power (award winners and award nominees) are tested against the consumer reviews (valence and volume) and box-office performance. Consumer review will be tested based mainly on the findings of Duan, Gu, and Whinston (2008a; 2008b) and Liu, Liu and Mazumdar (2014), which describe the endogenous character of review valence and volume.

This hierarchical regression model will analyze the effect of the control variables in the first block, and the independent variable in the second block.

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5. Results

In this paragraph the quantitative results of the data analysis will be presented. In the next sections the descriptive statistics and the Pearson correlations between the variables of the data sample set will be displayed, followed by hypotheses testing, where the results of the regression analyses are outlined and evaluated.

5.1 Descriptive Statistics

Table 1 presents the correlation matrix, which shows the means, standard deviations, and inter-correlations of the quantitative variables in this study. Multiple regression analyses are performed to determine the relationship between star power, consumer reviews, and box-office performance.

In total this sample consists of 5641 data observation points for the variables movie revenues (‘Boxoffice’), award nominees (‘TotalNomAD’), award winners (‘TotalWinAD’), review volume (‘CountOfUser1’), and critics review score (‘Metascore’). For consumer review valence (‘User_Score’) 4852 observation points are available, and 2922 for the Movie Production Budget (‘Prodbudget’).

The correlation matrix (Table 1) shows that star power has weak positive linear correlation with box-office performance, with the values r(5641)=0.202 for award nominees and r(5641)=0.199 for award winners. This outcome gives prior support for the prediction that star power (‘TotalNomAD’ and ‘TotalWinAD’) has a positive relationship with movie revenues (H1).

Further the table also presents the correlations for box-office performance with the consumer review variables, which show no correlation for review valence (r(4852)=-0.005, ns). However, for review volume a value of r(5641)=0.557 is found, which indicates a moderate positive correlation. In addition a very weak correlation can be observed between review valence and volume, r(4852)=0.046. These three results are in line with the predictions by Duan, Gu and Whinston (2008a; 2008b), which expects review valence to have no direct effect on box-office performance, in contrast to review volume (H2).

The correlation matrix (Table 1) also outlines the relationship between the variables of star power (“TotalNomAD” and “TotalWinAD”) and consumer reviews (“User_Score” and “CountOfUser1”). It can be seen that award nominees have a

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correlation with review valence, r(4852)=-0.051. In contrast to the first, the latter relationship is rather unexpected, since being award nominations indicate excellence in movie performance and therefore should intuitively yield positive reviews. The correlations of award winners with review valence and volume show the same picture: Award winners depict a weak correlation with review volume, r(5641)=0.168, as well as a very weak negative correlation with review valence, r(4852)=-0.038. For the same line of reasoning, this outcome was unexpected (H3 and H4)

Preliminary findings also show the inter-correlations of the control variables “Prodbudget” and “Metascore” with movie revenues. These indicate a strong positive correlation for production budget, r(2922)=0.672, and a very week positive one for critics review scores, r(5641)=0.078, which both are expected according to the literature in the theoretical framework.

Overall, these results of the correlation analysis may be interpreted as preliminary evidence, which mainly support the proposed hypotheses.

Table 1: Correlation matrix and descriptive statistics

Variable name Mean SD 1 2 3 4 5 6

1 Boxoffice 28594171.78 54642527.42 2 TotalNomAD 2.48 4.224 .202** 3 TotalWinAD 0.62 1.375 .199** .722** 4 User_Score 6.855 1.3328 -.005__ -.051** -.038** 5 CountOfUser1 1055.24 2880.751 .557** .207** .168** .046** 6 Prodbudget 37112824.48 41179886.56 .672** .218** .191** -.099** .473** 7 Metascore 54.882 17.5754 .078** .027*_ .032*_ .567** .165** -.016

** Correlation is significant at the 0.01 level (2-tailed). _* Correlation is significant at the 0.05 level (2-tailed).

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5.3 Hypotheses Testing

In this section, the outcome of the tested hypotheses will be presented as well as the results of the corresponding regression analyses.

5.3.1 The influence of star power on box-‐office performance (H1)

To test this first hypothesis, the first block of independent variables is utilized for the control variables ‘Metascore’ and ‘Production Budget’ whereas the second block contains the predictive variables ‘TotalNomAD’ and ‘TotalWinAD’. The model is significant (F=631.95, p<0.001) and explains 46.4% of variance. The results are visible in Table 2: Stars and directors with award winnings have a significant effect on movie revenues (β=0.063, p<0.001), whereas stars and directors with award nominations have no significant effect on box-office performance (β=0.10, p=0.596). Therefore H1a is supported, whereas H1b is not supported.

5.3.2 The effect of consumer reviews on box-‐office performance (H2)

The model, which predicts the effect of review valance on review volume, significantly explains 23.2% of the variances (F=428.17, p<0.001). The independent variable ‘User_Score’ is controlled by the variable ‘prodbudget’ (production budget). The regression analysis yields a significant effect of review valance on review volume (β=0.094, p<0.001), giving support for H2a.

Further to establish the relationship between customer reviews and box-office performance, a regression of the independent variable ‘CountOfUser1’ (review volume) is performed on the dependent variable ‘boxoffice’ (movie revenue), where the variables ‘prodbudget’ and ‘Metascore’ (critics review) are controlled for. This variable explains 52.7% of the variance (F=788.98, p<0.001) and the outcome of the regression indicates a statistically significant effect of review volume on box-office performance (β=0.301, p<0.001), and therefore supporting H2b.

5.3.3 The effect of star power on consumer reviews (H3 & H4)

In order to investigate a possible mediation effect of consumer reviews on the ‘star power – movie revenue’ relationship, the effects of star power on consumer reviews must be analyzed first. Since reviews entail two components, the effects on review

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valance will be analyzed firstly, because the evidence suggests that it positively influences review volume, as shown previously in H2a.

For the analysis of the effect of star power on reviews valence, a regression of the independent variables ‘TotalNomAD’ and ‘TotalWinAD’ (award nominations and winnings) is performed on the dependent variable ‘User_Score’ (review valence), where the variable corresponding to production budget is controlled. The results of the analyses show that both effects are insignificant with a p-value of 0.2 for stars with nominations, and 0.845 for stars with awards. From this it can be inferred that H3a and H3b are not supported.

The second component of consumer reviews entails the total volume of given reviews. The same regression as above-mentioned is performed on the dependent variable ‘CountOfUser1’ (review volume). The analysis yields that stars/directors with award winnings have no significant effect on review volume (β=0.006, p=0.802), whereas stars and directors with only award nominations have a significant effect on the quantity of given reviews (β=0.106, p<0.001). Therefore H4a is not supported, and H4b is supported.

Main effects β Sig.

Award Winners → Movie Revenues .063 <.001

Award Nominees → Movie Revenues .100 .569

Consumer Review Valance → Consumer Review Volume .094 <.001

Consumer Review Volume → Movie Revenues .301 <.001

Award Winners → Consumer Review Valance .005 .845

Award Winners → Consumer Review Volume .006 .802

Award Nominees → Consumer Review Valance -.035 .200

Award Nominees → Consumer Review Volume .106 <.001

Sig. levels are two-tailed.

Table 2: Standardized path coefficients.

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5.3.4 Mediation effect of consumer reviews (H5 & H6)

The analysis in section 5.3.2 has revealed that there is no support for a relationship of star power and review valence (H5a & H5b). Due to this it can be inferred that no mediation through the valence of consumer reviews is taking place (Baron & Kenny, 1986). Therefore both H5a and H5b are not supported.

Section 5.3.3 and the presented second analysis has shown that stars and directors with award winnings have no significant effect on the volume of consumer reviews. Equal to the above-mentioned line of reasoning, it can be inferred that consumer reviews do not mediate the effect that award winners have on box-office performance. Due to this, H6a is not supported.

H6b proposes that review volume mediates the effect of award nominees on box-office performance. However no significant direct effect of stars and directors with nominations on revenues has been found (section 5.3.1). Nevertheless as shown in section 5.3.3, the support of H4b indicated that nominees do have a significant effect on review volume of consumer reviews. This variable in turn has been shown to influence box-office performance (section 5.3.2), which gave support for H2b. Through these significant links of effects it can be inferred that the volume of consumer reviews has a indirect mediation effect on the relationship of award nominees and box-office performance (Baron & Kenny, 1986). Therefore H6b is supported.

Lastly, a Sobel Test is not required, since no significant direct effect has been

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6. Discussion

In the following section, the results presented previously are interpreted and discussed. The outcome of the proposed hypotheses are addressed and analyzed through the earlier-presented literature. This is then followed by practical implications, limitations and suggestion for further research, and the final conclusion of this research paper.

6.1 Tested Hypotheses

The aim of this study is to gain further insights in the effect of star power on movie revenues, and how consumer reviews may mediate this effect. Further, this research has expanded on previous studies by analyzing the element of star power in the categories of award winners and nominees, which is unprecedented in literature. The propositions of this research were constructed on the foundation of signaling theory (Kirmani & Rao, 2000; Connelly, Certo, Ireland, & Reutzel, 2011). Being product quality signals, it was predicted that star power and consumer reviews have a positive effect on movie revenues. In addition it was expected that consumer reviews mediate the effects of star power on box-office performance. To test these hypotheses, a quantitative multiple regression analysis with mainly secondary data was performed. The variables, which were investigated, are award winners, nominees, audience review valence, and volume of reviews.

Hypothesis 1 aimed to confirm the findings of previous literature, that star power in a movie positively influences box-office performance. The results revealed that star power indeed has a positive effect, which is in line with the findings of precedent studies outlined in the theoretical framework (Ravid, 1999; Elberse, 2007). However this study has expanded on the variable star power by separately analyzing award winners and nominees, and the results showed that only award winners have a significant effect on movie revenues. Award nominees on the other hand have seem to have no significant effect on box-office performance.

The purpose of hypothesis 2 was to reaffirm the findings on e-WOM and consumer reviews, especially those by Duan, Gu and Whinston (2008a; 2008b), which showed that reviews are endogenous in nature. As predicted, review valance has no direct significant effect on box-office performance whereas review volume does. Further the indirect effect of consumer review valence has been shown since it

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positively influences consumer valance, which in turn enhances movie revenues (Duan, Gu, & Whinston, 2008a; 2008b; Luo & Zhang, 2013).

Hypotheses 3 and 4 established the relationship of star power and its capability of creating positive consumer review valance and volume. According to the previous research it was expected that both award winners and nominees should yield higher valance and volume, either due to the large budget their movies receive (De Vany & Walls, 1999), their excellence in their performance (Elberse, 2007), or their ability to generate substantial WOM and buzz (Duan, Gu, & Whinston, 2008b; Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011; Liu, Liu, & Mazumdar, 2014). The outcomes of this study overall confirm the findings of previous literature (Liu Y. , 2006; Duan, Gu, & Whinston, 2008a; 2008b; Karniouchina, 2011), but additionally give further insights in the variable star power, since the effects on consumer reviews differ between award winners and award nominees.

This research shows that award winners have neither a significant effect on consumer valance nor volume, whereas award nominees only have a significant positive effect on review volume. This finding is rather unexpected since it seems to contradict the predictions based upon signaling theory, which states that star power is used as a quality signal (Kirmani & Rao, 2000; Connelly, Certo, Ireland, & Reutzel, 2011). Intuitively award winners and nominees should both qualify as quality signal, or one could even believe that stars and directors who won an award should show superior properties over stars that merely received nominations. However, the results show that the opposite is the case, and only award nominees have a significant positive effect on review volume.

A possible explanation for this outcome may lie in the timing of the award ceremonies and the announcement of the nominations. Candidates for an award can be speculated due to an excellent performance in a role, and the lists of nominees are presented preceding the awarding ceremony. These two factors may spark discussions and debates among the public and the media, generating a great amount of WOM and buzz. In line with the findings by Duan, Gu and Whinston (2008a; 2008b), this increased awareness effect may be the reason for the increased volume of consumer reviews.

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The role of consumer reviews, as a mediator of the effect of star power on movie revenues, was analyzed through hypotheses 5 and 6, and the before-mentioned findings and lines of reasoning are extended. Again, this research has expanded on previous literature by presenting the effects on movie revenues through star power in tandem with consumer reviews, and the specification of star power into two categories. The predictions of previous research state that the power of stars mainly unfolds through WOM and buzz and their mediating role (Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011; Liu, Liu, & Mazumdar, 2014), which is reflected by the volume and valance of consumer reviews.

As outlined above, award winners have neither an effect on review valance nor volume. This means that for award winning stars/directors, no mediation through consumer review takes place, although having a significant direct effect on movie revenues as shown in H1a. Therefore H5 does not apply, and thus the predictions do not hold true for the star power of award winners.

On the other hand stars and directors who were nominated for an award do have a significant effect on review volume (H4b). As Duan, Gu and Whinston (2008a; 2008b) and this research showed, the factor of volume is what positively affects box-office performance. Thus, despite not having a direct effect on movie revenues itself, award nominees do influence box-office performance indirectly through the volume of consumer research. Therefore the predictions of previous research seem to hold true (Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011; Liu, Liu, & Mazumdar, 2014). Further, this study reveals that the power of stars, on consumer reviews and revenues, unfold solely through award nominees, and not award winners. The reason for this is likely similar to the above-mentioned one, where nominations trigger discussion among the public and spark WOM and buzz, which in turn correlates to higher sales (Luo & Zhang, 2013).

In conclusion, the proposed hypotheses and their results overall confirmed the theories and predictions of existing literature, that were outlined in the literature review and conceptual framework. Unprecedented findings by this study are mainly related to the effect of star power, which differs among award nominees and winners. Here, award winners exclusively have a direct effect on box-office performance, whereas award nominees indirectly affect movie revenues, through its effect on consumer review volume, which positively affects movie sales.

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6.2 Managerial Implications

This study and numerous research has shown the importance of signals and WOM in today’s market place and especially in creative industries, such as the motion picture industry. A popular question by the public is, whether it is worth hiring a costly star and whether the seemingly enormous salary is justified. From this among other findings, high profile stars and directors are worth their high price, since they are a key factor in generating WOM, which contributes to a film’s financial success. This is also in line with findings of (Liu, Liu, & Mazumdar, 2014), that show that financers are more willing to provide funds if a known star is involved in a film project. DeVany and Walls (1999) confirm this by showing that movies with stars have overall higher budgets. This excess capital may then be used for commercial and promotion purposes, in order to further enhance WOM. In addition, as Sefert et al (2009) suggests, the combination of traditional advertisement and WOM have a substantial effect on consumer purchase decision. Managers should be aware of the buzz-generating power that stars entail, especially during the launch, or the first weeks of a movie (De Vany & Walls, 1999; Chakravarty, Liu, & Mazumdar, 2010; Karniouchina, 2011).

This star-generated buzz has practical implications for managers in other settings as well, since signaling theory and the effects of WOM also apply in other industries. As suggested by previous literature and the findings of this paper, the significant element of a star is not exclusively his/her superior performance, but also the capabilities to capture the attention of the public, increase awareness, spark discussion and buzz (Duan, Gu, & Whinston, 2008a; 2008b; Karniouchina, 2011).

Lastly, the management of reviews should be closely aligned with WOM- and buzz management, which are established techniques in the field of marketing, since both entail the characteristics of volume and rating level and its management. Because these variables and elements (WOM and reviews) are not exclusively confined to the movie industry, marketers in other fields may utilize the findings of this paper to possibly enhance revenues.

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6.3 Limitations & Suggestions for Further Research

This research has some limitations that could be addressed in future researches. Firstly, this research is built in a cross sectional format. In the movie industry, where weekly sales data are available, a panel data analysis that measures the weekly effects could yield more insights. Further, this research has focused solely on awards given by the Academy Awards (“The Oscars”) and the Golden Globes, and only in 3 different categories. In future research, more types of awards and categories could be included, which may shed light on the reason why award winners and award nominees differ in their effects. Also, further signal types could be included in future studies: Since the underlying mechanism for increased revenues seems to be the volume of WOM, other measurements for star power should be taken into account, which capture WOM and buzz (apart from award nominations). For instance, one could consider the social media presence and influence of stars as predictors for box-office performance, since these are also channels that generate e-WOM.

7. Conclusion

This study was conducted to further understand the effects of star power on box-office performance, and how WOM, namely consumer reviews, mediates this relationship. The contribution of this research to the existing literature lies in the unprecedented expansion of the star power variable, where the effects were analyzed for award winners as well as nominees. Through thorough literature review several predictions were established based on the foundation of signaling theory. These predictions were generally matched, but the specification of star power into two categories resulted in more detailed findings than previous literature. The results showed that only award winners and review volume have a direct effect on movie revenues, whereas award nominees only have an indirect effect on box-office performance through their influence on review volume. Thus, overall the results of this research confirm the proposed predictions, but further demonstrated an indirect effect of award nominees. An explanation for this phenomenon may lie in the underlying concept of WOM and buzz creation, which future research may further investigate. This additional information may be critical for practical implication, since the variables addressed in this research may decide the success or failure of a movie or possibly a general product in the market place.

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Acknowledgement

I would like to express my full gratitude to all the people who help me complete this Bachelor thesis and supported me along the way. First, I thank my supervisor Dr. Frederik Situmeang, who sparked my interest in the field of Marketing and guided me through the writing process of this thesis. Further my thank goes to Inge Wolsink, whose excellent teaching in the previous quantitative research course prepared me exceptionally well for this research paper.

Also I want to thank Jack, Jim, Lucas and Max for their input, feedback, and most of all, their friendship. Special thanks to my sister and my beloved parents, who always supported my decisions, and who made my education and all of this possible.

And lastly, I would like to thank Maureen, who made my stay in Amsterdam truly worthwhile, and who always encourages me to be the best version of myself.

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The hidden potential of star power and movie awards in the motion picture industry : a study of signaling theory, word-of-mouth, and consumer reviews as mediators for the effect of star power on box-office performance