I Heard It Through the Digital Grapevine: an Empirical Investigation of the Effects of Electronic Word-Of-Mouth On

I HEARD IT THROUGH THE DIGITAL GRAPEVINE: AN EMPIRICAL INVESTIGATION OF THE EFFECTS OF ELECTRONIC WORD-OF-MOUTH ON

SALES IN THE FILM INDUSTRY

Master’s Thesis Bart Penris | 6178545 MSc Business Administration | Entrepreneurship & Management in the Creative Industries Supervised by: Ms. I. Rozenthale Second reader: dr. B. Kuijken June 24, 2016

STATEMENT OF ORIGINALITY

This document is written by Student Bart Penris 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.

Acknowledgements

In the past five months a great deal of my time went to this thesis. It is by far the most extensive, complicated, challenging but also the most satisfying project I have done during my years at the University of Amsterdam. Completing this project therefore truly feels like a personal milestone. I do however realize that it would not have been possible without the help, support and understanding of several people. I would like to thank a few of them.

My supervisor, Ms. Ieva Rozentale, for her advice, creative thinking and useful feedback throughout the whole project.

Mattijs Grannetia from Boxofficenl.net, for providing me with the full 2014 – 2015 FDN dataset.

Jaco van de Pol, Emile Pater and Leon Horbach for their participation in the coding process.

My girlfriend for her overall support and the many days we spend together in the library.

My friends for their advice and encouragements.

My brothers for their support and understanding.

And lastly, my father - who is no longer with us - and mother, to whom I owe everything.

Abstract

Consumers who participate in discussions and post their opinions on products online engage in electronic word-of-mouth (eWOM) communications. eWOM is believed to be an important factor in explaining sales of experience goods because it is perceived as an independent source of information free of corporate interests. The aim of this study was to analyze how the volume and valence of eWOM from multiple social media sources affected the sales of experience goods in the setting of the Dutch film industry. Furthermore, because earlier research indicated that the effects might be different depending on the type of film, we made the distinction between the categories mainstream, niche and unclear. We used a sample of

363 films to study these effects with multiple regression analyses. We checked for differences in the pre-release and cumulative period to better isolate the effects. Our findings suggest that volume is the most important eWOM factor in explaining sales in both time periods and for all film types, while positive valence only matters in the cumulative period. Contrary to what we expected, we found no important differences for the effectiveness of eWOM volume between mainstream and niche film types. Positive eWOM valence appears to be only significant for mainstream and unclear film types, while critic ratings only matter for niche films. Interestingly enough we also found that negative eWOM valence can sometimes be beneficial in the pre-release period, especially for films that do not classify as a typical mainstream or niche offering. This study finds further evidence for the awareness effect of eWOM as found in earlier research and especially contributes in the sense that it used a social media monitoring tool to collect eWOM data on a very large scale from various sources.

Simultaneously, our findings and implications indicate that further research is needed to gain additional insight in the dynamics between eWOM, experience goods and commercial performance.

Table of Contents

List of tables ...... 7

List of figures ...... 7

1. Introduction ...... 8

2. Literature review...... 10

2.1 Creative goods as experience goods ...... 11

2.2 Critics: a traditional third party ...... 12

2.3 eWOM ...... 14

2.4 eWOM on social media ...... 16

2.5 Volume and valence of eWOM ...... 18

Volume ...... 19

Valence ...... 20

2.6 Mainstream versus niche ...... 22

3. Research design and Methodology ...... 25

3.1 Research design ...... 26

3.2 Data collection ...... 27

3.3 Dependent variables ...... 30

3.4 Independent variables ...... 30

3.5 Moderating variable ...... 31

3.6 Control variables ...... 32

3.7 Method of analysis ...... 33

4. Results ...... 36

4.1 Descriptive statistics ...... 36

4.2 Correlations ...... 39

4.3 Hierarchical regressions ...... 40

Main effects ...... 40

Interaction effects ...... 43

4.4 Split file regression ...... 44

5. Discussion ...... 47

5.1 Findings and theoretical foundations ...... 48

Volume ...... 48

Valence ...... 49

Film type ...... 50

5.2 Limitations, suggestions for future research and contributions ...... 53

5.3 Practical implications ...... 56

6. Conclusion ...... 57

References ...... 59

Appendices ...... 65

Appendix A: List of films used in final sample ...... 65

Appendix B: Instructions for Coders ...... 72

Appendix C: Coosto output for ‘Maleficent’ ...... 73

List of tables

Table 1: Descriptive statistics & ANOVA………………………………………………....…37

Table 2: Post-Hoc tests.…………………………………………………………………..…..38

Table 3: Correlations…….……………………………………………………………………40

Table 4: Regression analysis – Opening Weekend B.O.…………………...…………..…….42

Table 5: Regression analysis – Total B.O.……………………………………………………43

Table 6: Regression analysis – Split file……………………………………………………...47

Table 7: Summary of results………………………………………………...... ………...…48

List of figures

Figure 1: Conceptual Model……………………………………………………………….…25

1. Introduction

“All motion pictures are a gamble. Anything having to do with creating something that nobody’s seen before, and showing it, and counting on 10 or 20 million people, individuals, to go into the theater to make or break that film... that’s a gamble” (cited in: Lampel &

Shamsie, 2000, p. 239).

This quote by film director Steven Spielberg demonstrates the high uncertainty that surrounds both producers and consumers of creative goods. Creative goods like films are extra sensitive to such uncertainties because they can be classified as experience goods. While their counterparts, search goods, have certain characteristics that make them mutually comparable, the quality and value of an experience good can only be determined after buying and experiencing it (e.g. Cui, Lui & Guo, 2012). Thus, consumers look for alternative methods to assess the quality of an experience good before a purchase. A traditional way of doing this is by reading the reviews of professional critics in newspapers or magazines. Creative industries like art, theater and film all have dedicated critics who evaluate and advise the public on new releases and by doing so act as selectors in their industry (Wijnberg, 1995). Such critics are seen as third parties because they are supposed to be an independent source of information for consumers (Chen, Liu & Zhang, 2012).

Apart from seeking information from professional critics, consumers also mutually advise each other on different kinds of products. This process is called word-of-mouth (WOM; e.g. Day, 1971). The rise of the Internet increased the speed through which reviews and opinions spread across people from all over the world and simultaneously opened a whole new virtual platform for consumers to share their own opinions and experiences about products. These technological developments ensured that part of the discussions about products nowadays takes place online. This has led to a new term being used in academic literature: electronic word-of- mouth (eWOM). eWOM is similar to regular WOM, the difference being that eWOM happens 8

online, for instance through blogs, forums or review websites (e.g. Liu, 2006; Hennig‐Thurau,

Gwinner, Walsh, & Gremler, 2004).

The rise of social media in particular has accelerated and extended these dynamics.

Social media have made it even easier for consumers to post opinions and reviews online themselves and by doing so act as selectors for their own social environment. Consumers who engage in such activities should not solely be seen as consumers, but also as an external third party that deserve the attention of managers. While regular websites already offered these possibilities, they often require an additional registration before people are allowed to post or discuss anything. Social media have lowered the threshold to participate in this process by allowing and even encouraging real-time sharing of content and text on any given subject, without requiring an additional registration. This may lead to the expression of opinions and statements that are closer to people’s real-life feelings and beliefs (Hennig-Thurau, Wiertz &

Feldhaus, 2014).

Scholars have been exploring the links between person-to-person communications and business outcomes in many different settings. Both WOM and eWOM effects are believed to be of great importance for the sales of experience goods (e.g. Liu, 2006; Chevalier & Mayzin,

2006). However, in almost every study on this matter the actual eWOM data is collected from a single source, such as a dedicated forum or website. To our knowledge there are very little studies on the impact of eWOM on business outcomes in which the eWOM data is derived from multiple social media sources. Because social media eWOM may have different implications for business outcomes, this opens up an interesting gap for a study, which is formulated in the following research question: how does multiple source eWOM relate to the box office sales of films?

Earlier studies suggest that the extent to which a third party affects the success of a creative good is also partly dependent on the type of meta category the good belongs to (e.g.

Gemser, Oostrum & Leenders, 2007). Creative goods can be typically divided into two ‘meta’ categories: one consisting of popular - or mainstream - products, and the other one containing more niche products (Brynjolfsson, Hu & Simester., 2011). To our knowledge this implication has only been studied in the context of professional critics, and not yet with multiple source eWOM. This leads to the second research question: how is the effect as described in the first research question different for mainstream and niche films?

The setting of this study is the Dutch motion picture industry. This industry is particularly interesting for a study on eWOM because films are frequently discussed among consumers and highly sensitive to word-of-mouth interactions (Eliashberg, Jonker, Sawhney &

Wierenga, 2000). We use a sample of 363 films released during a two-year period in cinemas all across the country. We distinguish between the pre-release and cumulative time period, both on the side of the dependent variable (box office) as the independent variable (eWOM), in order to isolate the effects of eWOM on sales in two more specific timeframes. Next to the theoretical contributions, this study also informs managers in the creative industries on the implications of multiple source eWOM on the commercial performance of different types of creative goods.

Therefore, it can help with the development of digital marketing strategies in creative industries.

This thesis is structured as follows: first, an overview of the literature on creative goods and eWOM will be given. Second, the specific research design, data collection and methodology of this study are stated. Third, the results and findings are presented and lastly, the limitations and managerial implications of this study are covered, as well as suggestions for future research.

2. Literature review

In this chapter previous academic research on the topics covered in this thesis will be explored.

First, the specific characteristics of creative goods and the role of both traditional and emerging

third party influencers will be elaborated on. Second, we will move to eWOM and specifically eWOM on social media and the different effects that volume and valence are believed to have on sales. Lastly, the distinction between mainstream and niche in the context of the film industry will be explored.

2.1 Creative goods as experience goods

Creative goods like films traditionally classify as experience goods. These goods have to be experienced (and thus, bought) in order to determine the quality. Furthermore, even after buying an experience good, any value judgement will be subjected to personal preferences of the consumer (Cui et al., 2012). As Caves (2003, pp. 74) states: “Producers make many decisions that affect the expected quality and appeal of the product, yet their ability to predict its audience's perception of quality is minimal”. This is called the ‘nobody knows’ principle, which simply means that nobody really knows the true value of a creative good. (Caves, 2003).

Furthermore, because consumer preferences are unstable, production decisions depend on educated guesswork and prior success of a good does not provide reliable guidelines for new products (Lampel & Shamsie, 2000). This leads to a high uncertainty for producers and entrepreneurs in these industries, because the likeability and success of the products they release will affect both their artistic reputation and financial situation.

At the same time consumers of experience goods face uncertainties as well. When consumers for example go to a cinema, they pay upfront without knowing if they are going to like the product. If they do not like it, there is no chance of returning the product and getting a refund. Therefore, potential consumers search for ways to assess the value and quality of an experience good before a purchase in order to reduce their uncertainty. Towse (2011, pp. 213-

214) notes that there are three levels on which this uncertainty reduction happens. First, there are advertising messages in which producers make claims on the features of their goods. 11

However, advertising is often treated with skepticism and sometimes even leads to a higher uncertainty because these claims can still not be verified without purchasing the product

(Lampel & Shamsie, 2000). Second, consumers sometimes can get limited access to experience goods in the form of trials and previews. Listening to snippets of new music and playing demos of videogames are examples of this, but this is not possible for every product. Third, consumers use publicly stated preferences of others as predictors of their own experience.

This last method is crucial because it is often the only available independent source of information on experience goods, especially when the option of a preview or trial is not available, like is the case with films. The individuals or groups who publicly state their opinions and preferences about experience goods can be seen as surrogate consumers who take on the task of reviewing products that may be of interest for the target group they reach out to (Hirsch,

1972). This means that the value of experience goods is often subjectively measured through a set of preferences and standards of these groups or individuals that act as selectors in the market

(Wijnberg & Gemser, 2000). They form third parties who communicate their opinions about experience goods to potential consumers. The assumption that such third parties are often the only independent source of information available puts them in a position of power, and in fact

“they become participants in the strategic rivalry that shapes the industry” (Lampel & Shamsie,

2000, p. 238).

2.2 Critics: a traditional third party

Traditionally, the only way for potential consumers to get an indication of the quality of an experience good was through the printed reviews of professional critics. In such reviews, critics talk about the background of the concerned good, the content and typically end with a value judgement (Brown, Camerer & Lovallo, 2012). The reviews are published in newspapers, magazines and online and often appear just before the goods enters the market. In order to have 12

the review published prior to the release, critics are sometimes given the chance to get exclusive access before regular consumers.

Because of the timing of the reviews and the value judgement they contain it is no surprise that scholars in the past hypothesized that critics have an effect on the success of the product they review. The specific effects and directions seem to be partially dependent on the type of product and the industry. Friberg and Grönqvist (2012) found a positive effect of favorable reviews of a wine on the sales of that particular wine. A study by Heiman (1997) shows a similar result for restaurants. For books however, research indicates that mutual disagreement of critics and the expression of extreme judgements, positive or negative, has a positive effect on sales (Clement, Proppe & Rott, 2007). A more recent study on reviews in the book industry suggests that negative reviews can even enhance sales when prior awareness about the book is low (Berger, Sorensen & Rasmussen, 2010).

The effects of critic reviews have also been studied in the context of the film industry.

Litman (1983) finds that critic rating is one of the three most important determinants of cumulative box office, next to the use of a major distributor and a Christmas release. Wallace,

Seigerman and Holbrook (1993) also conclude that critics influence sales, and claim that the relationship is U-shaped. This means that for positively reviewed films higher ratings lead to more revenues, but also that films with the most negative ratings earn more revenues than films with mixed reviews. Their finding partially conflicts with the study of Wanderer (1970), who argues that the tastes of critics are similar to that of audiences. However, there has also been research that suggests positive reviews have no significant influence at all (Ravid, 1999).

A crucial question that is addressed in multiple papers on critics in the film industry is whether the possible effect that their reviews have on box office sales should be interpreted as a prediction effect or as an influencer effect (Eliashberg & Shugan, 1997; Gemser et al., 2007;

Basuroy, Chatterjee & Ravid, 2003; Reinstein & Snyder, 2005). The difference between the

two is that critics as predictors only predict the success in terms of box office of a to-be-released film, while critics as influencers actually influence people to go and see the film. Both potential effects are isolated by looking at the effects of reviews on opening-weekend box office

(influencer effect) and cumulative box office (prediction effect).

Eliashberg and Shugan’s (1997) findings suggest the existence of a prediction effect but they find no evidence for an influencer effect. Interestingly, this is somewhat contradictory to the results of Basuory et al. (2003), as they find evidence for both the prediction and the influencer effect. Reinstein and Snyder (2005) also find both effects to be significant, but agree with Eliashberg and Shugan that the prediction effect is more important. They also add that a higher perceived expertise of the critic can strengthen both effects, and that the significance of the effects is dependent on the type of film. This last finding is confirmed by Gemser et al.

(2007) and its implications will be discussed in section 2.6.

It appears that scholars in the past had mixed conclusions about the influence of critics on the sales of experience goods. In this study the focus will not be on critics and their role as selectors and third party influencers of the public. However, the above listed findings may still be of great importance because they do indicate the importance of third parties for experience goods and can help to hypothesize the effects of other parties than professional critics on sales in the creative industries.

2.3 eWOM

Now that the majority of the western world has access to the internet, the reviews of professional critics are widely available to consumers. A much more radical result however is that consumers now have the chance to also write reviews and statements in a non- professional manner. The mutual communications of consumers about products online is called electronic word-of-mouth (eWOM).

The roots of eWOM can be found in the concept of word-of-mouth (WOM). We speak of WOM when there is oral, person-to-person communication regarding a brand, product or service between a communicator and a receiver (Amdt, 1967). Crucial in this matter is that the receiver perceives the communicator as non-commercial, because this is how WOM distinguishes itself from advertising. Generally, WOM is believed to be of great importance for sales. Sheth (1971) even claims that when the objective is to raise awareness for an innovation or to persuade the public in trying a product, WOM is more important than advertising and critics. WOM is believed to be so effective because the receiver perceives the source of information as credible and trustworthy (Day, 1971). Bone (1995) adds that WOM is free from corporate interests and marketing intentions. Because experience goods are often consumed collectively and feature in daily conversations, WOM is believed to be of even greater importance in creative industries (Eliashberg, Elberse & Leenders, 2006).

The rise of the internet has created a new dimension in person-to-person communications in a virtual world. This new, some may say modernized way of WOM is called eWOM. It can be defined as “any positive or negative statements made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Henning-Thurau et al., 2004, pp. 39). These statements are typically ventured through blogs, forums or review sites, but can also take on other forms. eWOM builds on the same basis as WOM, while the fact that it takes place online brings some additional implications. The online character of eWOM allows people who engage in these discussions to maintain their anonymity (Lee & Youn, 2009). This leads to a situation where consumers are more comfortable with expressing their true opinions and experiences

(Goldsmith & Horowitz, 2006).

The subjective nature of experience goods suggests that eWOM still does not have the ability to objectively inform consumers on the features and quality of such goods. However,

eWOM can raise the expectations and create a certain buzz around a product. When looking at the film industry in particular, studios sometimes even heavily count on positive WOM / eWOM in their marketing strategies for films. Some films are released on a relatively low number of screens and the studio plans to gradually expand the number of screens following the positive eWOM a film gets. This release strategy is called a platform release (e.g. Reinstein

& Snyder, 2005). In this case eWOM is also part of the marketing mix by feeding discussions and generating awareness at the target group. The specific implications of eWOM on business outcomes will be discussed more extensively in section 2.5.

Chu and Kim (2011) argue that the eWOM behavior of consumers has three building blocks: opinion seeking, opinion giving and opinion passing. The first group contains consumers who search on the internet for information and opinions before they make a purchasing decision. The second group represents the traditional concept of an opinion leader.

As discussed earlier, this can be a professional critic, but also regular consumers who are for example active on product review sites or forums. The third group of opinion passing consumers are especially important for eWOM, because opinion passing behavior leads to the wide availability of opinions and corresponding discussions on the internet (Dellarocas, 2003).

2.4 eWOM on social media

Social media channels like Facebook, Twitter and Instagram provide individuals with new opportunities to engage in discussions with their peers about products, brands or services on the internet. They are designed to spread and share content with the users’ network within seconds.

Therefore, they are believed to facilitate and further enhance the process of opinion seeking, opinion giving, and especially opinion passing behavior that form eWOM (Chu & Kim, 2011).

Peer-to-peer communications through social media also have the advantage that the sources of information in this case already belong to or can be identified through someone’s network (Chu 16

& Kim, 2011). While this diminishes the potential advantage of anonymity in eWOM communications, it enhances the perceived credibility and trustworthiness of the communicator

(Chu & Kim, 2011; Kim, 2014).

A crucial difference between ‘regular’ eWOM and eWOM on social media lies in the motivations for users to participate and engage in discussions. The channels of regular eWOM such as dedicated websites and forums are likely to be used by consumers who are actively seeking information, advice or opinions on a specific product. This is confirmed by Hennig-

Thurau et al. (2004), who find that advice seeking, positive self-enhancement, concern for other customers and economic incentives are all positively related to both the visit frequency and the number of comments written on a web-based opinion platform. Goldsmith and Horowitz (2006) also find that financial risk reduction factors are important factors for engaging in online discussions, and thereby confirm the suggestion that participants in ‘traditional’ eWOM often have specific goals to pursuit.

The motivations for social media usage are more casual than for regular eWOM because for many people social media are part of their everyday life (Kim, 2014). Therefore, the goal of social media usage is not to find opinions on specific products like is the case with a regular eWOM source. Furthermore, while forums and dedicated review websites often require an additional registration or membership before one can actively and fully participate in discussions, social media are already used by a majority of the western world, which means that all of their users can freely share information and engage in discussions with their peers (Kim,

2014). Social media interactions are therefore closer related to offline WOM. Hennig-Thurau et al. (2014) describe eWOM on social media almost as a lovechild of eWOM and WOM. They claim that it has the personal connection between sender and receiver, real-time transmission, continuous feedback and push and pull options characteristics of offline WOM, while the fact that it entails written communication and the receiver is a potentially very large group relates

to eWOM. Furthermore, they argue that social media messages are perceived by receivers as more honest and without an agenda than the traditional, more closed eWOM sources. Kim

(2014) uses the same line of reasoning by suggesting that eWOM on social media has the credibility and trustworthiness advantages of offline WOM combined with the easy availability of eWOM.

Because of these reasons, eWOM on social media better represents customers’ perspectives on experience goods and potential consumers will use this as a source while selecting and seeking products (Kim, 2014). Studying eWOM implications through multiple social media sources is therefore more likely to give a comprehensive representation of how people feel and act and it is more effective when trying to capture the behavior of the human being in its natural habitat.

2.5 Volume and valence of eWOM eWOM can be interpreted and measured in multiple ways. Two characteristics of eWOM that have been used a lot in research are volume and valence. The volume of eWOM indicates the attention a product receives online and can be expressed in the number of messages (Hennig-

Thurau et al., 2014) or quantity of information available for potential buyers (Cheung &

Thadani, 2012). The valence, or sentiment, of eWOM indicates whether the message contains a positive or a negative value judgement (e.g. Godes & Mayzlin, 2004; Chen et al., 2012; Cui et al., 2012). The volume and valence of eWOM are also the main concepts studied in this thesis. In this section, studies on the effects of volume and valence are reviewed and based on these earlier findings hypotheses are formulated.

Volume

Overall, the volume of eWOM is believed to be an important determinant of sales. Liu (2006) studied the effects of eWOM in the form of content on Yahoo Movies on the box office sales of films. He found that eWOM is most active in the week prior to the release of a film and that especially the volume of eWOM significantly relates to box office performance. Furthermore, he found that pre-release eWOM volume has significant explanatory power over opening weekend box office, and continues to do so for aggregated box office. This confirms the idea that eWOM volume can create a certain buzz which is beneficial to both initial and cumulative sales. Kim (2014) extends Liu’s research by taking multiple source eWOM into account. Her findings also suggest that the volume of eWOM positively relates to box office performance.

Duan, Gu and Whinston (2008) speak of an ‘awareness effect’ created by the volume of online postings about films which significantly influences box office.

Research in the setting of other creative industries has also highlighted the importance of eWOM volume. Dhar and Chang (2009) show that the volume of eWOM in the form of blog posts is positively correlated with future music sales. Amblee and Bui (2011) found that for e- books, the volume of eWOM has the power to convey the brand and product reputation and influence sales. For the sales of regular books on the internet the volume of eWOM also shows to have a significant impact (Chevalier & Mayzlin, 2006). These findings are further confirmed by Cui et al. (2012), who find that for experience goods the volume of user reviews matter the most in predicting sales.

Because the volume of eWOM is believed to be an important predictor of cumulative sales but also has the power to create a buzz in the pre-release period, we can formulate the first two hypotheses:

H1a: The volume of eWOM in the pre-release period has a positive effect on opening

weekend box office sales.

H1b: The total volume of eWOM has a positive effect on cumulative box office sales.

Valence

The impact of eWOM valence has also been studied in a wide range of industries and settings.

Lee and Youn (2009) state that negative user reviews are harmful to the extent a consumer would be willing to recommend retail products to friends. Additionally, Chevalier and Mayzlin

(2006) found that the negative impact of new one-star reviews on book sales is greater than the positive impact of new five-star reviews. Park and Lee (2009) claim that while negative eWOM damages experience goods, positive valence does no good. They argue that negative information magnifies consumers’ uncertainty and fear that derives from their poor cognitive knowledge about the product. These findings contribute to the notion of negativity bias, which simply suggests that people weigh negative information more than positive information (e.g.

Rozin & Royzman, 2001). A possible explanation for the existence of the negativity bias is that positive reviews are more attributed to the personal preferences and characteristics of the reviewer, rather than the actual product experience (Chen & Lurie, 2013).

Other scholars have conflicting findings. Sonnier, McAlister and Rutz (2011) find that both positive valence (positively) and negative valence (negatively) of eWOM influence sales.

However, their study is limited to search goods and therefore not useful for building our conceptual model. Cui et al. (2012) and Liu (2006) find that for experience goods the valence of eWOM does not matter at all, while Vermeulen and Seegers (2009) studied the effects of eWOM in the hotel industry and find that both positive and negative eWOM increases consumer awareness, especially for lesser known hotels. They even suggest that the positive eWOM does

more good than negative eWOM hurts, which directly conflicts with the concept of the negativity bias.

When looking at the particular case of eWOM in the film industry there is also no clear consensus on the impact of valence. Hennig-Thureau et al. (2014) only find a significant negative effect on sales for negative eWOM valence. Liu (2006) finds no significant effect between valence of eWOM and box office. Duan et al. (2008) have a similar conclusion. They do however suggest that although eWOM valence itself does not directly influence sales, there is an indirect effect on sales because more positive eWOM can lead to a higher eWOM volume.

Dellarocas, Zhang and Awad (2007) show that positive eWOM valence is a significant predictor of future performance. Kim (2014) also finds a significant association between positive eWOM valence and box office sales. This idea is reinforced by Chintagunta, Gopinath and

Venkataraman (2010), who state that positive eWOM valence is crucial in predicting box office sales.

Although the proposed impact of eWOM valence is not undisputed and sometimes even denied, it appears that studies where the methodological and theoretical approach is most congruent with ours tend to conclude that a higher level of positive eWOM leads to higher box office sales, in one way or another (Duan et al., 2008; Kim, 2014). Therefore, we expect similar results for positive eWOM valence as we do for eWOM volume. This expectation leads to the following two hypotheses:

H2a: Positive valence of eWOM in the pre-release period has a positive effect on opening weekend box office sales.

H2b: Positive total valence of eWOM in has a positive effect on cumulative box office sales.

2.6 Mainstream versus niche

We already elaborated on the major differences that separate search goods from experience goods. A typical further distinction that is made in many creative industries is the distinction between two ‘meta-categories’: mainstream and niche. Mainstream creative goods are expected to attract a large audience and gross high revenues, while niche creative goods are more specialized, and targeted at a smaller, often more intellectual audience (Brynjolfsson et al.,

2011). By using this distinction, the results of the current study also become more generalizable to other creative industries than the film industry because the mainstream - niche categorization can be found in almost every one of them.

Before hypothesizing the different effect of eWOM for both types of films it is important to clarify the boundaries between the two. Scholars have used different approaches to do so in the past. Categorizations have been made based upon intrinsic features of the film itself, for example the content, genre or narrative structure (e.g. Bordwell & Thompson, 1997) or the presence of special effects and movie stars (e.g. Bagella & Bechetti, 1999). Other scholars have used extrinsic features, like the height of the production and marketing budget (Holbrook &

Addis, 2008) or the size and identity of the distribution company responsible for distributing the film (Zuckerman & Kim, 2003).

Gemser et al. (2007) build upon the approach used by Zuckerman and Kim (2003). They determine whether a film should be considered mainstream or niche by looking at the type of cinema a film is being released in. Cinemas are used instead of distributors because Dutch film distribution companies often do not specialize in only one type of film (Gemser et al., 2007).

Although at the time this may have been a reasonable approach, one must take into account that the Dutch cinema landscape has changed in the last years. The largest mainstream cinema operator that is currently active in the Netherlands, Pathé, introduced Pathé Alternative Cinema

(PAC) in late 2005. The PAC program offers a more niche selection and responds to the

growing demand for quality films (Pathe, 2016). At the same time new cinemas have opened that don’t specifically focus on only one type of film, but rather offer both mainstream and niche films.

These recent developments in the Dutch cinematic landscape can be seen as a result of the emerging relevance of the concept of cultural convergence (e.g. Shrum, 1991). Advocates of this concept state that the once clear differences between high and popular art diminish due to the growth of the middle class in society. This makes it questionable to divide films into mainstream and niche on the basis of fixed factors like cinemas, studios or budgets. Therefore, in this study we chose to use coders to make the distinction between film types out of the belief that the best way a distinction between mainstream and niche films can be made nowadays is through the subjective opinions of regular consumers; the same subjective opinions that ultimately judge on the aesthetic value of those goods (Lampel & Shamsie, 2000).

To our knowledge, no studies have been done on the specific effects of social media eWOM volume and valence on mainstream and niche films. A study by Yang, Kim L., Amblee and Kim W. (2009) considers the effect of eWOM on mainstream and non-mainstream films in

South Korea. While they find no significant difference for film type, their results do not form a good basis for this research because of the use of single source of eWOM in the form of a film- related website, in a similar way Liu (2006) and Duan et al. (2008) did earlier. The different film types have however been examined in the context of film critics. In the beginning of this chapter the prediction and influencer effects of such critics are discussed. Reinstein and Snyder

(2005) show evidence for the existence of an influencer effect, especially for films with platform releases. Because of this limited release strategy, platform releases are often associated with niche films. Gemser et al. (2007) have a similar finding. They conclude that for mainstream films, critics have a prediction effect, while for niche films critics can actually influence the box office performance.

While none of these two studies uses eWOM as a predictor variable, its findings could still be of very good use for this study. Austin (1983) notes that the people who visit niche films tend to agree more with critics than the mainstream audience does. This could suggest that niche consumers also agree more with their fellow fans and therefore are more likely to be influenced by pre-release eWOM from each other. We therefore expect that the pre-release and post- release effects that are associated with the predictor and influencer effect of critics will hold for eWOM. This leads to the following two hypotheses:

H3a: For niche films, the effects of pre-release eWOM volume and valence will be

significant for opening weekend box office, while the effects of total eWOM volume and

valence on cumulative box office will be not significant.

H3b: For mainstream films, the effects of total eWOM volume and valence will be

significant for total box office, while the effects of pre-release eWOM volume and valence

on opening weekend box office will be not significant.

The different effect that eWOM potentially has on mainstream and niche films can be linked back to the long tail theory (e.g. Brynjolfsson et al., 2011). This theory states that because of the rise of the internet, it should become easier for the more obscure and niche products to gain attention and boost their sales. This would suggest that a higher level of especially eWOM volume would be extra beneficial for niche movies. Dellarocas and Narayan (2007) note that the volume of eWOM messages is higher for mainstream products because people tend to post more on these subjects because of the interplay with their peers. According to De Meyer (2012) this indicates that if producers of niche products would find a way to increase the eWOM volume on their products, this would be beneficial for their sales. This leads to the final hypothesis and the presentation of our conceptual model in figure 1:

H3c: The volume of eWOM will have a stronger effect on the box office of niche films, compared to mainstream films

Volume

eWOM Box office sales

Valence

Type of film

Figure 1 Conceptual model

3. Research design and Methodology

In this section the research methods used to test the different hypotheses in this thesis are elaborated on. First, the overall research design and setting are discussed. Secondly, the data collection method, the used sample and the measurement of the variables are described. We conclude with presenting the specific methods of statistical analysis that were used to test the hypotheses.

3.1 Research design

In this study, the aim is to analyze in what way multiple source eWOM relates to opening weekend and cumulative box office and how this is different for mainstream and niche films.

Because we attempt to answer our research questions through hypotheses testing, the nature of our research is deductive and we use a quantitative approach. Furthermore, all of our variables come from existing databases, and are not further manipulated by the researcher. This prevents any potential bias that can normally arise from human interactions with constructs and variables. Of course, the only exception is our film type variable, which was classified by human coders.

Regular offline WOM has always been a very difficult concept to capture effectively in a measurable variable. Earlier attempts to do so have relied on surveys and controlled laboratory experiments (Dellarocas, Awad & Zhang, 2004). Both of these methods have their own flaws.

Surveys have been widely used to measure WOM in different contexts because it allows a researcher to ask respondents detailed questions about their personal communication habits and behavior. Mahajan, Muller and Kerin (1984) for instance use a survey design research to examine the effectiveness of different introduction strategies for new products in the context of positive and negative WOM. The main downside of using a survey to measure WOM is the error that lies in the self-reporting of behavior. Simultaneously, lab experiments have an issue with the generalizability of the results exactly because of the controlled, unnatural setting of the research (Dellarocas et al., 2004). The digitalization of modern society opened up possibilities to measure interpersonal communication in a more precise and authentic manner because these communications are preserved. Our database method therefore allows us to derive eWOM data on a much larger scale from multiple sources at the same time.

3.2 Data collection

For this research we used a dataset from the Dutch Film Distributors Association (FDN). This dataset contained all the films released in Dutch cinemas in 2014 and 2015. This initial sample of 388 films was coded by three coders as follows: 1 = mainstream, 2 = niche, 3 = unclear.

The coders were instructed to classify each film based on the appeal it had on them and the way they thought the film was positioned in the market. They were instructed to classify the film as mainstream when they thought the film was meant for average, mainstream moviegoers and most related with a typical blockbuster. They were instructed to classify a film as niche when they felt like the film was targeted at a specialized target group who are interested in films that offer more quality and depth and most related to arthouse. When they did not know which category a film should belong to they were instructed to classify that film as unclear. The specific instructions for coders is listed in appendix B. Coding was done digitally in an excel file which contained the film titles, their release dates and a link to their IMDB page. When at least two out of three coders assigned a film to the same category, the film was classified as such. The films that the coders did not agree upon, meaning that it got conflicting classifications, were classified as unclear. The films that were classified as ‘3’ by a majority of the coders were given the same classification.

As stated in the section 2.6, this approach was used because earlier used methods to divide films in two meta categories (e.g. production budget, production company, type of cinema) are insufficient at this point in time. Both production companies and cinemas no longer stick to one particular type of film and are therefore no reliable sources for film type classification. Furthermore, the opinion of coders can be used as a proxy for the public opinion on film category, and in the end it is the public that fits a creative good in a particular frame based on content, appeal and social context (Currid, 2007).

Most of the scholars who have researched the effects of eWOM in the film industry have used a single source to collect the eWOM data from. Liu (2006), Duan et al. (2008) and

Chintagunta et al. (2010) all used user-posted messages about films from Yahoo Movies as a proxy for eWOM on the films in question. At the time it made sense to use this source because it was seen as the most popular movie website, it did not require a fee for access and the structure of the website was convenient for data collection (Liu, 2006).

While Yahoo Movies removed the user-generated reviews from their website a couple of years ago, nowadays there are some other options for websites to use as a proxy for eWOM on films. Perhaps the most famous website for user-ratings of films is IMDB, which has a database of nearly every film accompanied with an average user rating. There are several reasons why such an approach is less suitable for this research. First of all, IMDB gets visitors from all over the world, while in this research I aim to analyze the impact of Dutch eWOM on

Dutch box office sales. A source that would satisfy that particular condition is Moviemeter, a

Dutch website on which users can post their opinions about movies and give them a one to five- star rating. However, then the question still arises to what extent the data is representative for the total amount of eWOM; an issue that every single source method has to cope with (Kim,

2014). Although Dellarocas et al. (2004) provide some survey-based evidence that online ratings can serve as a proxy for WOM, as of today it is technically feasible to capture online

WOM on multiple sources simultaneously.

The eWOM data in this study is derived from Coosto, a SaaS tool that allows to search the volume and valence of written content from multiple social media sources on a given subject in a certain period of time. The tool works similar to Radian6 as used by Kim (2014), with the difference being that Coosto solely finds content generated by Dutch users. Really interesting about this tool is the wide range of popular social media sources it can use in a search. The eWOM data in this research is aggregated from the most frequently used social media channels

in The Netherlands: Twitter, Facebook, YouTube, Instagram and Pinterest. This multiple source approach reduces potential bias single source data potentially suffers from, considering the possible homogeneity in demographics and interests of visitors and users of specific websites

(Kim, 2014). Furthermore, multiple source eWOM should give a more representative view of the overall volume and valence of content on a given subject.

For every film I used the following basic search query:

"FILM TITLE~" NEAR film* OR bios* OR zien OR gezien

The command NEAR orders the software to find content where the input term is present within a maximum of ten words of one of the terms stated to the right of the command. A star (*) next to a word is used to also find extensions of that given word. For this particular query that means that the software for instance will also find the words filmbezoek (film visit) and bioscoop

(cinema). Furthermore, when a tilde (~) is placed next to a word the software will also find words that slightly deviate from the given term. This is a useful method to control for spelling errors in film names that contain words that are easily misspelled, which is very useful since most of the eWOM is user generated and by no means official company content.

The used search query probably did not find every single Dutch message on social media that exists on the subject. After all, not every post about a film has the film title within ten words of the words film*, bios*, zien (see) or gezien (seen). This addition to the query was however necessary to make sure that all of the findings were actually related to their corresponding films.

When the film title Irrational Man was used as a query without the NEAR addition, Coosto found content of different subjects than the actual film while for a very specific title like Child

44 this was not the case. However, because the addition to the query was necessary to avoid an unjust volume of measured eWOM for certain films, the addition was used for all of the films in the sample in order to avoid biased results.

In line with my expectations beforehand it appeared that, even with the addition to the query, some of the film titles failed to isolate eWOM content solely about that particular film

(N = 25). Examples of such titles were Life and Dope. These were removed from the sample, leaving a final sample of 363 films. The final sample of films is listed in appendix A.

3.3 Dependent variables

The goal of this study was to analyze the effects of fixed, moderating and control factors on the commercial performance of films. The commercial performance of films was measured by box office data. We derived all the box office data from FDN dataset. This dataset listed both the opening weekend and the total box office performance. The opening weekend performance of a film was defined as the revenue of the Thursday, Friday, Saturday and Sunday in a film’s opening week, while total box office constitutes the total cumulative revenue a film has made in Dutch cinemas. Earlier research suggests different effects of independent variables on opening weekend and total box office, especially when taking different film types into account

(e.g. Gemser et al., 2007). Therefore, both variables were included in our dataset as OPENBOX and TOTBOX.

3.4 Independent variables

Earlier findings suggested that the volume of eWOM messages peaks around the theatrical release of a film (Kim, 2014; Liu, 2006). Because of this assumption, for every film in the final sample eWOM volume and valence was collected from the eight weeks before the release up to one day before the Dutch premiere date and from the eight weeks after the release, starting on the day of the premiere. We did not include eWOM content from later than eight weeks after the premiere because it would be likely that the majority of that content would not affect box office performance for two reasons. First, the amount of eWOM quickly decreases after the 30

premiere date and after six to eight weeks the volume is almost equal to zero (Liu, 2006).

Second, on average films in Dutch cinemas are no longer shown than two months, so any eWOM that would come up after this period of time would not have had any effect on box office figures (BNR, 2011).

All of the eWOM data is derived from Coosto. The software states a summary of the findings with the total number of found messages, the amount of positive messages and the amount of negative messages, leaving the remaining messages as ‘neutral’. The valence of messages is automatically coded by Coosto’s algorithm. An example for pre- and post-release output for a film is presented in appendix C. By manually changing the search period for each film we were able to capture the volume and valence of both pre-release eWOM (resp.

PRE_VOL; PRE_POS_PERC; PRE_NEG_PERC) and total eWOM (resp. TOT_VOL;

TOT_POS_PERC; TOT_NEG_PERC). We transformed the valence variables into percentages using the function compute variable in SPSS.

3.5 Moderating variable

It was hypothesized that that the potential effects of eWOM would be different for mainstream and niche films. Although the moderating effect of this variable had not been directly tested before in this specific context, there are studies that point at the differences between types of films and the way they are affected by other external parties (e.g. Gemser et al, 2007; Reinstein

& Sneyder, 2005).

After the coding process was done, the moderating variable TYFILM was included in the dataset and was given a value of 1 for mainstream, 2 for niche and 3 for unclear. In order to use the film type variable as an interaction term in a regression analysis, dummy variables had to be made for the mainstream and niche categories. In the first one all the mainstream films

were given the value 1 while the rest was labeled as 0 while in the latter the same was done with niche films. The unclear films were used as a reference category.

3.6 Control variables

There are several factors that are useful to control for when analyzing the impact of eWOM on film box office. The number of screens a film is released on is arguably the most obvious one of them. After all, a film that is shown on more screens is able to draw more visitors and can therefore generate a higher revenue. In numerous earlier studies the variable has proven to be an important predictor of box office performance (e.g. Gemser et al, 2007; Liu, 2006, Basuroy et. al, 2003). The number of screens were derived from the FDN dataset and included in this study as the continuous variable SCREENS.

In order to run valid quantitative analyses, it was important that the film sample was large enough. We therefore chose to take the whole population of films released in Dutch cinemas in the years 2014 and 2015 and combine those two databases in one, large dataset. This dataset therefore contained both Dutch and foreign productions. This was used as a control variable because it can be argued that films of Dutch origin generate more eWOM in the

Netherlands. All of the films in the final sample were manually coded as a domestic (1) or international production (0), making up the dichotomous variable DOMESTIC.

Another factor that could have possibly affected box office are the opinions of film critics. In line with earlier research on eWOM (Liu, 2006; Duan et al., 2008; Chintagunta et al.,

2010), the aggregated opinion of film critics where therefore also included in the dataset as control variable CRITIC. For international productions this data was derived from the website

Metacritic.com, which aggregates a large number of reviews from US critics and states the average value judgement as a number between 0 and 100. Because only a few of the Dutch productions were featured on this website, we manually calculated the average critic rating for 32

those movies by using four well-known sources for professional film reviews in The

Netherlands (De Volkskrant, Nu.nl, De Telegraaf, Het Parool). Because these sources all used a 1-5 star rating system, we manually recoded the Dutch ratings into the same scale as the international releases by using the following formula: amount of stars * 20.

3.7 Method of analysis

All the data was collected in an Excel file and exported to SPSS statistics version 20 in order to execute the statistical analyses. There were no missing values in the dependent, independent and moderating variables. Almost all of the control variables were also free from missing values, with the exception of CRITIC. For a few films it appeared that there were not enough critic reviews available with an explicit stated value judgement. Because the number of films for which this was the case was so small (N = 4), and the concerned variable was not of crucial importance to the study, in all of the conducted analyses pairwise deletion was used. This means that the data of the four films that had a missing value was still used in the analyses (Peugh &

Enders, 2004).

We ran descriptive statistics on all of the variables in the dataset and checked for significant differences between the three different film types using one-way ANOVA’s. To check exactly which of the three groups’ means differed significantly from each other we ran additional post-hoc tests. Almost all of the variables had a very high standard deviation relative to their mean. This indicated a wide spread of observations within those variables.

Consequently, when running the ANOVA’s for each of the displayed variables it appeared that

Levene’s test for equality of variances was significant for all the variables except the control variable CRITIC. We therefore used Welch’s robust F test for every variable except CRITIC because this test is able to cope with the combined effects of unequal sample sizes and

heterogeneity of variances. For the same reasons we used Tukey’s post-hoc test for CRITIC and the Games-Howell post-hoc tests for the rest of the variables (Clinch & Keselman, 1982).

Because both the independent and the dependent variables were measured on a continuous scale, the hypotheses were tested using multiple regression analyses. The use of multiple regressions allowed to check for the effect of multiple independent variables on a dependent variable in different steps and also allowed for an interaction-effect check. This was done using two separate hierarchical regression analyses, one for opening weekend box office and one for cumulative box office, with three levels each. Hierarchical regression analyses were particularly useful in this study because it allowed us to isolate the effects of the eWOM variables after controlling for other variables (Woltman, Feldstain, MacKay & Rocchi, 2012).

Exploration of the variables showed that both the dependent variables, all the eWOM variables as well as the control variable SCREENS had a high, positive skew and were not normally distributed. While at least for the independent variables normality is no necessity, the cone shaped residual plots of the regressions visually showed that the skewed variables caused heteroscedasticity issues. This appears when the variances of a variable highly differ among levels of another variable that predicts it. In combination with an unequal sample sizes this can lead to invalid analyses results and the drawing of false conclusions (van Peet, Namesnik &

Hox, 2012). To overcome this problem, all the skewed variables were transformed using natural logs which removed both the heteroscedasticity and skewness issues. Because some films had zero negative eWOM and log transformations do not work with values of zero, all the valence variables were transformed using the formula: lnX = ln(x+1) (Berry, 1987). The new variables were included in the dataset as lnOPBOX, lnTOTBOX, lnPRE_VOL, lnPRE_POS_PERC, lnPRE_NEG_PERC, lnTOTVOL, lnTOT_POS_PERC, lnTOT_NEG_PERC and lnSCREENS.

The downside of using log transformations was that the interpretation of the regression results became trickier. The interpretation of the unstandardized B coefficients of the log transformed independent variables was different than the B´s of the untransformed variables because they were no longer shown in their original units (LaLonde, 2005). However, the betas of independent variables in the same regression analysis could still be used to compare the strength and direction of the effects of the different independent variables because beta coefficients measure the effect size of variables in terms of standard deviations (van Peet et al.,

2012). We therefore chose to report only the standardized beta coefficients in all of the regression tables.

A set of interaction variables was computed in SPSS to check for interaction effects.

This was done by multiplying all of the eWOM variables with both the MAINSTREAM and

NICHE dummy variables, using the unclear films as reference category. This lead to a total of twelve interaction variables. However, it appeared that for both of the film types all of the newly created interaction variables highly correlated with each other (lowest correlation r = 0.78, p <

0.01). This caused multicollinearity issues as some of the variance inflation factors (VIF) values were way above the generally accepted limit of 10 (lnOPBOX highest VIF = 33.28; lnTOTBOX highest VIF = 33.97) (Belsley, Kuh, & Welsch, 1980). To overcome this problem, we chose to only include one interaction variable per type of film, and use this as a proxy for the other interaction variables. For both mainstream and niche films we used the interaction with lnPRE_VOL, as this variable had the highest correlations with the other variables (resp. mean r = 0.92 and mean r = 0.91).

To further explore the interactions between the different film types and the eWOM variables, we ran additional linear regressions for each of the three groups separately. We used

SPSS’s ‘split file’ function to compare the results of our regressions for each of the three groups.

This method was earlier used by Gemser et al. (2007) to determine the different effects of critic

reviews on opening weekend and cumulative box office for different types of films. Split file regressions do not allow for the regression coefficients to be compared across groups because it in fact runs separate analyses for each of the groups. However, when the sample size is large enough it can be a useful method to get a clearer overview of potential different impacts of independent variables on a dependent variable for multiple groups.

4. Results

This chapter gives an overview of the results of the statistical analyses. First, the descriptive statistics of the dataset are presented. Second, the correlations between the variables in this study are shown and discussed. The chapter is concluded with the results of both the hierarchical and the split file regression analyses.

4.1 Descriptive statistics

The 363 films in our final sample generated an aggregated grand total of €84.483.466 and

€461.567.403 in opening weekend and cumulative revenue respectively. The film City of

Violence had the lowest opening weekend with €8614 while Spectre’s €3.377.404 opening weekend was the highest of all. The lowest cumulative box office in our sample was the €22.712 acquired by Victoria, with Spectre again topping the list with a total box office result of

€20.427.555. Interesting to note is that City of Violence and Victoria were labeled as niche and

Spectre as mainstream. Furthermore, 56 of the 363 films (15.4%) were domestic productions.

Niche films received an average 13.8 and 15 points higher critic rating than mainstream and unclear films respectively.

In total, the data of 368.502 eWOM messages have been included in this study. Most of the messages were posted after the premiere date, with the total pre-release messages being

139.838 (37.9%). 36

Table 1 shows a descriptive summary of the variables in the final sample. The largest

part of the films in our sample were labeled as mainstream (43.8%), followed by niche (28.7%)

and unclear (27.5%). The table shows that most of the released films were foreign productions,

that they were released on an average of just under 70 screens and received an average critic

rating of 57.6. An average film generated a cumulative revenue of approximately 5.5 times its

opening weekend revenue. What struck about the eWOM variables was that the vast majority

of the messages were generated during the post-release period and that on average, positive

eWOM outnumbered negative eWOM by a landslide both in the pre-release period and in the

total period of sixteen weeks the data was collected.

Table 1 Descriptive statistics and ANOVA results

Total (N = 363) Mainstream (N = 159) Niche (N = 104) Unclear (N = 100) ANOVA

M SD M SD M SD M SD F

DOMESTIC 0.15 0.36 0.20 0.03 0.10 0.03 0.14 0.04 2.96t

SCREENS 69.6 46.2 101.1 46.5 36.6 23.3 53.8 28.2 110.20**

CRITIC 57.6 17.7 54.0 16.4 67.8 17.9 52.8 16.3 27.36**

OPBOX € 232.737 € 362.492 € 417.598 € 481.096 € 70.271 € 69.373 € 107.771 € 87.695 42.99**

TOTBOX €1.271.536 €2.185.559 €2.278.343 €2.952.309 € 422.143 € 488.492 € 554.080 € 633.641 30.34**

PRE_VOL 385.2 653.8 579.6 853.9 231.4 412.5 236.14 337.898 11.32**

PRE_POS_PERC 21.8% 10.3% 20.2% 9.0% 23.1% 10.2% 23.0% 11.8% 3.70*

PRE_NEG_PERC 3.8% 3.7% 4.0% 3.6% 2.8% 2.7% 4.5% 4.4% 7.84**

TOT_VOL 1015.2 1559.8 1556.3 2053.7 579 870.7 608.5 734.4 15.56**

TOT_POS_PERC 28.0% 9.8% 27.1% 8.7% 29.5% 9.8% 28.0% 11.1% 2.06

TOT_NEG_PERC 4.5% 3.4% 4.6% 3.4% 3.5% 2.8% 5.4% 3.7% 9.39** t p <0.10; * p < .05 level (2-tailed); ** p < .01 level (2-tailed)

The ANOVA results show that for every variable with the exception of DOMESTIC

and TOT_POS_PERC there were significant differences between means of the three types of

films. In this study, we were particularly interested in the differences between mainstream and

niche films. It appeared that on average, mainstream films were released on a larger number of screens, generated more opening weekend and cumulative revenue as well as more eWOM volume, both in the pre-release period and in total. On the other hand, niche films received higher critic ratings and significantly less negative eWOM in both periods.

Table 2 shows the results of the post-hoc test for each of the variables by factor

TYFILM. The means of the three film types did not differ significantly from each other for every variable with a significant F result. However, for every variable with a significant F result there were significant differences between mainstream and niche films, the two groups of which the potential differences were of particular interest in this study. The unclear films differed significantly with one of the other two groups alternately. Exceptions were

SCREENS and OPBOX, where all three groups differed significantly and PRE_POS_PERC, where Welch’s F test was significant but the post-hoc tests showed that there were only marginal differences between specific groups.

Table 2 Post-hoc tests for the differences between film types

Mainstream - Niche Mainstream - Unclear Niche - Unclear

p p p Test

DOMESTIC 0.04* 0.40 0.60 Games - Howell

SCREENS 0.00** 0.00** 0.00** Games - Howell

CRITIC 0.00** 0.85 0.00** Tukey

OPBOX 0.00** 0.00** 0.003** Games - Howell

TOTBOX 0.00** 0.00** 0.22 Games - Howell

PRE_VOL 0.00** 0.00** 0.99 Games - Howell

PRE_POS_PERC 0.05t 0.08t 0.99 Games - Howell

PRE_NEG_PERC 0.01* 0.61 0.003** Games - Howell

TOT_VOL 0.00** 0.00** 0.69 Games - Howell

TOT_POS_PERC - - - -

TOT_NEG_PERC 0.01* 0.15 0.00** Games - Howell t p <0.10; * p < .05 level (2-tailed); * p < .01 level (2-tailed)

4.2 Correlations

Table 3 shows the correlations between the variables. The two dependent variables lnOPBOX and lnTOTBOX had a very high positive correlation (r = 0.91, p < 0.01). The same applied for the independent variables lnPRE_VOL and lnTOT_VOL (r = 0.94, p < 0.01), lnPRE_POS_PERC and lnTOT_POS_PERC (r = 0.68, p < 0.01) and lnPRE_NEG_PERC and lnTOT_NEG_PERC (r = 0.74, p < 0.01). This can be explained by the composition of those variables: all of the ‘TOT’ variables were operationalized as cumulative indicators and therefore also contained the data of the ‘PRE’ variables. In this case the high mutual correlations were not a problem because the concerned variables were not used in the same regression analyses.

It turned out that lnTOT_VOL had a strong positive correlation with lnSCREENS (r =

0.58, p < 0.01) and both lnOPBOX (r = 0.68, p < 0.01) and lnTOTBOX (r = 0.73, p < 0.01).

In any case the number of screens a film was released on seemed to be an important variable for this study due to its strong positive correlations with both the dependent and independent variables. The correlations indicate that a higher number of initial screens was associated with higher box office, higher eWOM volume and less negative cumulative eWOM.

The valence variables had interesting correlations as well. The total positive valence variable lnTOT_POS_PERC had a very weak but significant positive correlation with lnTOTBOX (r = 0.19, p < 0.01). However, this was not the case for the pre-release positive valence variable lnPRE_POS_PERC and lnOPBOX: although their correlation was very weak and not significant, interestingly enough it was also negative (r = -0.09). Surprisingly, negative valence in the pre-release period had a very weak but significant correlation with lnOPBOX (r

= 0.17, p < 0.01), but not with lnTOTBOX. This could indicate that especially in the pre-release period, it would be better to receive negative eWOM than receiving no eWOM at all.

The control variable DOMESTIC was very weakly but significantly correlated with the number of screens (r = 0.17, p < 0.01), total box office (r = 0.13, p < 0.05), pre-release eWOM

volume and negative valence (resp. r = 0.17, p < 0.01; r = -0.22, p < 0.01) and total eWOM

volume and negative valence (resp. r = 0.17, p < 0.01; r = -0.22, p < 0.01). Because domestic

films were labeled as 1, these correlations suggest that domestic films were released on more

screens, received more eWOM volume and less negative eWOM and generated more overall

revenue.

Table 3 Pearson's correlations coefficients

1 2 3 4 5 6 7 8 9 10 11

1 DOMESTIC 1

2 lnSCREENS 0.27** 1

3 CRITIC 0.04 -0.12* 1

4 lnOPBOX 0.01 0.75** -0.04 1

5 lnTOTBOX 0.13* 0.80** 0.07 0.91** 1

6 lnPRE_VOL 0.33** 0.53** 0.18** 0.58** 0.61** 1

7 lnPRE_POS_PERC 0.10 -0.03 0.12* -0.09 0.03 0.04 1

8 lnPRE_NEG_PERC -0.22 -0.03 -0.17** 0.17** 0.03 0.11* -0.06 1

9 lnTOT_VOL 0.28** 0.58** 0.22** 0.68** 0.73** 0.94** 0.08 0.12* 1

10 lnTOT_POS_PERC -0.03 0.04 0.20** 0.06 0.19** 0.01 0.68** -0.09 0.10 1

11 lnTOT_NEG_PERC -0.31** -0.13* -0.22** 0.14** -0.07 -0.01 -0.16** 0.74** -0.02 -0.18** 1 t p <0.10; * p < .05 level (2-tailed); ** p < .01 level (2-tailed)

4.3 Hierarchical regressions

The results of the hierarchical regression analyses are shown in table 4 for opening weekend

and table 5 for cumulative box office.

Main effects

To start with opening weekend box office, the first model contained only the control variables

lnSCREENS, CRITIC and DOMESTIC. The three control variables were already able to

explain a large part of the variance in opening weekend box office (R2 = 0.602, p < 0.01). The table shows that this was mainly due to the variable lnSCREENS, which had a very large positive beta (β = 0.47, p < 0.01). Additionally, DOMESTIC had a negative beta (β = -0.21, p

< 0.01) which indicates that foreign productions generated more opening weekend revenues than domestic productions. The control variable CRITIC was not significant in all three models.

In the second step the pre-release variables for eWOM were added to the model. Those variables explained an additional 7.9% of the variance in lnOPBOX and allowed for the testing of hypotheses 1a and 2a. Hypothesis 1a stated that a higher eWOM volume in the pre-release period would be positively related to opening weekend box office. The results show that this was indeed the case because a significant positive effect was found of lnPRE_VOL on the dependent variable (β = 0.30, p < 0.01). Hypothesis 1a was therefore confirmed.

Hypothesis 2a stated the existence of a similar effect for valence: it was expected that positive valence in the pre-release period would have a positive effect on opening weekend box office. Table 4 shows that this was not the case because the relationship between lnPRE_POS_PERC and the dependent variable was not significant. Interestingly enough, negative valence in the pre-release period did have a significant relationship with opening weekend box office (β = 0.10, p < 0.01). This indicated that, consistent with what we found in the correlations, a higher level of negative eWOM in the pre-release period leads to more opening weekend box office. However, because we found no significant results for positive valence hypothesis 2a was rejected.

Table 4 Three-level hierarchial regression analysis on dependent variable: lnOPBOX

Model 1 Model 2 Model 3 F= 178.78** F= 125.28** F= 104.54** R2= 0.602 R2= 0.681 R2= 0.705 Beta t Beta t Beta t (Constant) - 21.59** - 17.13** - 18.50** lnSCREENS 0.81 23.12** 0.65 17.43** 0.54 12.95** CRITIC 0.06 1.79 0.01 0.33 0.04 1.33 DOMESTIC -0.21 -6.07** -0.24 -7.23** -0.23 -7.17** lnPRE_VOL - - 0.3 7.61** 0.25 6.06** lnPRE_POS_PERC - - -0.05 -1.7 -0.04 -1.33 lnPRE_NEG_PERC - - 0.1 2.91** 0.08 2.57* main X pre_vol - - - - 0.19 4.36** niche X pre_vol - - - - -0.04 -1.13 t p <0.10;* p < .05 level (2-tailed); * p < .01 level (2-tailed)

Table 5 shows the results of the hierarchical regression analysis on cumulative box office. In the first model, the same three control variables were used as for the regression analysis on opening weekend box office. The control group again proved to be of high importance in explaining the variance in cumulative box office (R2 = 0.660, p < 0.01). Again, lnSCREENS was the most important factor in explaining the variance (β = 0.84, p < 0.01) and it appeared that foreign productions generated more revenue because of the negative beta of DOMESTIC

(β = -0.10, p < 0.01). This time, the average critic rating was also significantly related to the dependent variable (β = 0.17, p < 0.01), while this was not the case for opening weekend box office. However, this significant effect disappeared when the second group of variables was added to the model, which explained another 10.6% of the variance in cumulative box office.

Hypothesis 1b stated that the total eWOM volume would have a positive effect on cumulative box office sales, in the same way pre-release volume would affect opening weekend box office. The results show that the eWOM volume was again an important factor in predicting box office performance (β = 0.40, p < 0.01). This confirmed hypothesis 1b.

Hypothesis 2b stated that positive valence of eWOM would have a positive effect on cumulative box office sales. Where for opening weekend box office we only found a significant effect for negative valence eWOM, for cumulative box office this effect was not the same. Table

5 shows that a higher percentage of positive valence in total eWOM volume had a significant positive effect on cumulative box office revenues (β = 0.12, p < 0.01), while the negative valence variable was not significant this time. Hypothesis 2b was therefore confirmed.

Table 5 Three-level hierarchial regression analysis on dependent variable: lnTOTBOX Model 1 Model 2 Model 3 F = 232.42** F = 194.93** F = 104.54** R2 = 0.663 R2 = 0.769 R2 = 0.775 Beta t Beta t Beta t (Constant) - 21.37** - 10.67** - 11.10** lnSCREENS 0.84 25.97** 0.6 17.52** 0.55 13.99** CRITIC 0.17 5.54** 0.04 1.21 0.05 1.52 DOMESTIC -0.1 -3.09** -0.14 -4.78** -0.13 -4.61** lnTOT_VOL - - 0.4 11.46** 0.37 9.94** lnTOT_POS_PERC - - 0.12 4.59** 0.12 4.59** lnTOT_NEG_PERC - - 0.01 0.13 -0.001 -0.03 main X tot_vol - - - - 0.12 3.10** niche X tot_vol - - - - 0.01 0.31 t p <0.10;* p < .05 level (2-tailed); * p < .01 level (2-tailed)

Interaction effects

In the third model of the regression analyses on both of the dependent variables the two interaction variables were added to indicate whether the eWOM variables had different impacts on box office sales depending on film types. As explained in section 3.7, because of the high correlations between the three pre-release and the three cumulative variables only one of each was included in the model. The third model explained an additional 2.5% and 0.6% of the variance in opening weekend and cumulative box office respectively.

In hypothesis 3c we expressed the expectation that the effects of total eWOM volume on cumulative box office would be greater for niche films compared to mainstream films.

However, the third model of table 5 shows that the ‘niche X totvol’ interaction variable was not significant, while its mainstream counterpart was significant (β = 0.12, p < 0.01). This means that, contrary to what we expected, the effect of eWOM volume was greater for mainstream films. Hypothesis 3c was therefore rejected.

Model 3 of table 4 shows that we found similar results for opening weekend box office.

Remarkable was that this time, the niche interaction variable even had a small negative beta.

While this effect was not significant, it even further emphasized our finding that the eWOM variables are more effective in predicting box office success for mainstream films, compared to niche films. Because the volume interaction variables in both models served as a proxy for the valence variables we can conclude that the same interaction effects also stand for both of these variables.

We did not include an interaction variable for unclear films because in order to use and interpret a categorical variable with more than two levels in a regression analysis, it is only necessary to use k – 1 dummy variables, where k is the number of categories in the moderating variable (Hardy, 1993). We chose to use unclear films as a reference category because we were particularly interested in the differences between mainstream and niche films.

4.4 Split file regression

Table 6 shows the results of the separate regressions we ran after splitting our dataset on the variable FILMTYPE. The first model was again executed with only the three control variables.

The results show that, consistent with our hierarchical regressions, for all film types the variable lnSCREENS stayed an important predictor of both opening weekend and cumulative box office sales and domestic films seemed to have generated less revenue than foreign productions.

Average critic ratings were significant for every film type for cumulative box office, but only at the opening weekend for niche films (β = 0.26, p < 0.01), confirming the influencer effect for critics as earlier found by Gemser et al. (2007).

In the second model the eWOM variables were added to the model: the pre-release variables were used for the opening weekend dependent variable and the total variables were used for the cumulative dependent variable. The role of the control variables lnSCREENS and

DOMESTIC did not change in the second model: both were still significant for all film types.

Remarkably enough the third control variable CRITIC stayed only significant for niche films on both opening weekend (β = 0.17, p < 0.05) and cumulative (β = 0.13, p < 0.10) box office.

This shows that critic ratings were only significant influencers of the box office success of niche films.

Hypothesis 3a stated that for niche films, pre-release eWOM volume and positive valence would have a significant positive effect on opening weekend box office, while there would be no significant effect of eWOM volume and valence on cumulative box office.

However, table 5 shows us that this was not the case. For both opening weekend and cumulative box office the volume of eWOM had a significant positive effect, while valence did not seem to have any influence. Since for niche films no differences were found between opening weekend and cumulative box office, hypothesis 3a was rejected.

Hypothesis 3b claimed in some way the opposite of 3a: it was expected that for mainstream films, total eWOM volume and positive valence would have a significant effect on cumulative box office while the pre-release eWOM variables would have no effect on opening weekend box office. The results show that again, eWOM volume was significant for both opening weekend and cumulative box office. However, we did find a difference regarding the effect of eWOM valence for mainstream films: the results show that positive eWOM valence has a significant positive effect on cumulative box office (β = 0.17, p < 0.01) while this was

not the case in the pre-release period (β = 0.07, p > 0.10). Hypothesis 3b was therefore partially confirmed.

For the films in the unclear category we found that for cumulative box office, the same predictors were significant as for mainstream films. However, for the opening weekend dependent variable we also found a significant positive effect of pre-release negative eWOM valence (β = 0.20, p < 0.05), next to the already significant volume variable (β = 0.29, p <

0.01). Because we did not found this effect for the other film types. this suggests that the positive effect of negative pre-release eWOM valence on opening weekend box office that we found in the hierarchical regression (see table 4) could be mainly explained by the films who had the classification ‘unclear’.

Table 6 Split file regressions on dependent variables lnOPBOX and lnTOTBOX Mainstream Niche Unclear lnOPBOX lnTOTBOX lnOPBOX lnTOTBOX lnOPBOX lnTOTBOX

Beta t Beta t Beta t Beta t Beta t Beta t

(Constant) - 13.16** - 12.24** - 10.54** - 7.72** - 15.05** - 13.68**

lnSCREENS 0.55 8,23** 0.7 11.97** 0.9 13.29** 0.8 10.67** 0.6 6.78** 0.71 9.41** Model 1 CRITIC 0.1 1.57 0.12 2.16* 0.26 4.21** 0.37 5.32** 0.09 0.98 0.24 3.14** ------4.41** -0.1 -1.75t -3.81** -1.70t -2.28* -2.14* DOMESTIC 0.29 0.25 0.12 0.21 0.17 F 31.16** 58.67** 59.93** 42.85** 15.60** 33.46** R2 0.378 0.533 0.647 0.567 0.33 0.514

(Constant) - 9.92** - 4.98 - 9.92** - 5.31** - 11.12** - 8.50** ** ** ** ** ** **

lnSCREENS 0.47 7.16 0.55 9.46 0.76 9.60 0.53 6.40 0.5 5.59 0.47 6.37 Model 2 CRITIC 0.03 0.46 0.03 0.6 0.17 2.55* 0.13 1.71t 0.04 0.41 0.05 0.72 ------4.91** -2.78** -4.23** -2.52* -2.64* -2.23* DOMESTIC 0.32 0.14 0.29 0.17 0.24 0.16 ** ** ** lnPRE_VOL 0.37 5.52 - - 0.25 3.02 - - 0.29 3.01 - - - - -1.09 - - -1.13 - - 0.03 0.4 - - lnPRE_POS_PERC 0.07 0.07 * lnPRE_NEG_PERC 0.07 1.05 - - 0.02 0.25 - - 0.2 2.94 - - ** ** ** lnTOT_VOL - - 0.39 6.69 - - 0.46 5.49 - - 0.42 5.49 ** * lnTOT_POS_PERC - - 0.17 3.65 - - 0.08 1.24 - - 0.18 2.62 - - - - 0.07 1.12 - - -0.8 - - -0.75 lnTOT_NEG_PERC 0.05 0.05 F 25.52** 55.89** 34.30** 34.29** 12.17 29.46** R2 0.504 0.69 0.684 0.684 0.442 0.658 t p <0.10;* p < .05 level (2-tailed); * p < .01 level (2-tailed)

5. Discussion

In this section the implications of the results of our statistical analyses will be discussed. First,

a brief summary of the results of the hypotheses will be presented, together with the theoretical

foundations and possible explanations for unexpected results. Secondly, the limitations, and

contributions of this study and directions for future research will be discussed. The section

concludes with practical implications for managers in the creative industries.

5.1 Findings and theoretical foundations

In this study we investigated how multiple source eWOM affected the commercial

performance of films in Dutch cinemas. Furthermore, we analyzed whether these effects were

different for different kinds of films, mainly focusing on the distinction between mainstream

and niche films. To isolate the effects of eWOM on sales we used two different timeframes:

pre-release and cumulative, on both the dependent and the independent variables. Table 7

shows a summary of our results.

Table 7 Summary of results Hypothesis Result H1a Volume of eWOM in the pre-release period has a positive effect on opening weekend BO Accepted H1b Total volume of eWOM has a positive effect on cumulative BO Accepted H2a Positive valence of eWOM in the pre-release period has a positive effect on opening weekend BO Rejected H2b Positive total valence of eWOM has a positive effect on cumulative BO Accepted H3a For niche films, pre-release eWOM will be significant on opening weekend BO, no cumulative effect Rejected H3b For mainstream films, total eWOM will be significant on cumulative BO, no pre-release effect Partially accepted H3c Volume of eWOM will have a stronger effect on BO of niche films Rejected

Volume

We expected that the volume of eWOM would be positively related to box office, both in the

pre-release as in the cumulative period. This proved to be correct, consistent with our

expectations and almost every other study on the effects of eWOM on sales (e.g. Liu, 2006;

Dhar & Chang, 2009; Chevalier & Mayzlin, 2006). The regression analyses showed that for

all three of the film types the volume of eWOM had the greatest effect on box office, next to

the number of screens. One could argue that eWOM volume, number of screens and box

office are so tightly related because films that open on more screens draw more visitors,

which automatically leads to a higher eWOM volume. However, the fact that we found the

same results for the pre-release period - before the films had drawn any Dutch visitors at all -

indicates that the effects of volume on box office cannot solely be explained as a logical result of the higher number of visitors, but that the volume of eWOM in fact truly influences and predicts box office sales. A high volume of eWOM seems to create a certain buzz around a film in the pre-release period, which continues to do its work in the long run. This finding further emphasizes the importance of receiving attention and ‘being talked about’ in the creative industries and thus provides further evidence for the existence of an awareness effect function of eWOM volume (e.g. Liu, 2006; Duan et al., 2008). The fact that volume had the greatest effect of the eWOM variables furthermore supports the finding of Cui et al. (2012) that for experience goods, the volume of eWOM matters more than the valence in predicting sales.

Valence

With respect to the main effects of eWOM valence, we expected that a higher level of positive eWOM would be positively related to box office sales in both the pre-release and the cumulative period. This expectation was confirmed for the cumulative period and therefore in support of Kim (2014) and Dellarocas et al. (2007). This is an interesting finding because earlier studies reached far from a consensus on this point. Our findings conflict with both

Chintagunta et al. (2010), who found that only the valence matters and not the volume, and

Liu (2006), who found that valence of eWOM did not matter at all.

Our finding considering positive valence can have two explanations. First, it is important to note that both Chintagunta et al. (2010) and Liu (2006) used eWOM data from a single source. The strength of the current study is that we used an enormous amount of data from a wide range of social media sources. This data collection method may have given a more comprehensive and closer-to-reality view of how people interact about creative goods

(Kim, 2014). Second, it is possible that more positive eWOM affects revenues in an indirect

way by positively influencing the volume of eWOM (Duan et al., 2008). This would also explain why the positive valence variable was only significant in the cumulative period, because according to this assumption a higher level of positive eWOM would gradually lead to more revenues so that the actual effect would only be visible in the long run.

The results of the analyses also showed an unexpected significant effect regarding the valence of eWOM. Surprisingly enough, a higher level of negative valence was positively related to opening weekend box office, indicating that a higher level of negative messages in the pre-release period lead to higher early revenues. To our knowledge this is an empirical finding that has not yet been found by scholars in a similar setting and it implicates the opposite of the negativity bias that was described by a number of scholars, which suggests that negative messages are more harmful then positive messages do good (e.g. Rozin &

Royzman, 2001; Chen & Lurie, 2013; Hennig-Thurau et al., 2014). A possible explanation for this finding is that the negativity bias is often explained as an effect on cumulative or week- to-week sales. Any negative eWOM in the pre-release period cannot logically reflect peoples’ actual judgements about a film, which is a factor that is shown to diminish any potential influential effects of eWOM (Chen & Lurie, 2013). Prior to a film’s release, a higher level of negative eWOM may therefore contribute to the awareness effect in the sense that it drives the curiosity of consumers and subsequently attracts more opening weekend visitors. In the post- release period, negative eWOM is more likely to be attributed to the film and therefore, the significant effect disappears. However, negative eWOM valence never hurts the revenues: it only stops having its - unexpected - positive effect on box office.

Film type

Another important characteristic of this study is the fact that we considered the potential different effects for different types of films. We hypothesized that the influencer and

prediction effects for niche and mainstream films as found in the context of critics (Gemser et al., 2007) would hold for eWOM, because we expected that the earlier found effects could be attributed to differences between mainstream and niche audiences. In section 2.6 this is explained more extensively. We found that critic ratings were only significant for niche films and mainly in the pre-release period, which confirms the finding of Gemser et al. (2007). For eWOM however, the influencer effect for niche films did not hold and the prediction effect for mainstream films was only partially confirmed. Both these effects had not yet been studied in the same setting as the current study which means that there can be several explanations for our findings.

For niche films, there were no differences regarding how their box office was affected by eWOM in the two periods: the same variables were significant in explaining opening weekend- and cumulative box office. This suggests that for niche films only the volume of eWOM matters in explaining revenues. This could indicate some support for the long tail theory as it shows that a higher volume of eWOM leads to higher sales of niche products

(Brynjolfsson et al., 2011). However, for this theory to be confirmed, the effect of eWOM volume on sales should have been greater for niche films compared to the other film types, which is what we stated in the last hypothesis. The third model of the hierarchical regression analyses showed that this was not the case. This leads to the conclusion that while the volume of eWOM is important for niche films, it is not more important than for other types of films.

For mainstream films, we hypothesized that both eWOM volume and valence would only be significant for the cumulative period. This proved to be incorrect because volume was also significant in the pre-release period. We did however find some evidence for the prediction effect. For cumulative box office, we found an additional significant effect of positive eWOM valence, next to the already significant volume variable. This brings us to the question: why do positive eWOM messages lead to higher box office for mainstream- but not

for niche films? A possible explanation could be the role of professional critics. Our results show that critics only have significant explanatory power over the box office of niche films, which suggests that niche audiences attribute more value to the opinions of critics than mainstream audiences do. This could indicate that for niche films, the opinions of critics more or less ‘replace’ the opinions of other consumers online, consistent with what Austin (1983) found.

Lastly, the films that were labeled as unclear had an ambiguous role in our study. The post-hoc tests already showed that the means of the unclear films were congruent with one of the two other film types on most of the independent variables, while mainstream and niche films significantly differed for every variable except pre-release positive valence (see table 2).

This was not the case for the regression analyses: the effects of the independent variables on the box office revenues of unclear films were largely consistent with the other two film types.

However, there were two interesting findings considering unclear films. First, in the cumulative period, the percentage of positive eWOM was significant, just like for mainstream films. This further emphasizes the importance of critics for niche films (see preceding paragraph). Second, in the pre-release period, negative eWOM positively affected the opening weekend box office sales of unclear films, while this was not the case for mainstream and niche films. This suggests that the significant main effect for negative valence on opening weekend box office (see table 4) can be fully attributed to films in the unclear category, which means that especially for these films, negative valence prior to the release contributes to the awareness of the public. After the release, when consumers are more likely to attribute value judgements to the actual product instead of consumer characteristics, the significant effect of negative eWOM disappears while positive eWOM valence becomes significant, just like for mainstream films.

5.2 Limitations, suggestions for future research and contributions

In this study there are several factors that have to be considered when drawing conclusions.

The use of Coosto for eWOM data offered exciting new possibilities but it also created some snags. First of all, the query may not have given the best results for every film. Some film names are more likely to be named in combination with one of the interaction terms in the query than others. We explained this in section 3.2. Second, we used only the film title as basis for the query. If a film has a famous director or star actor, people may be likely to use that as a reference in a post on social media. For example, people may have referred to the film Magic in the Moonlight as “Woody Allens’ new film” or to Fury as “the new film starring Brad Pitt”. These messages were not included in our dataset.

Also, one of the main reasons we used Coosto was that it allowed us to capture the thoughts and sentiment of real people. However, because the software collected messages from all sorts of social media accounts, it also included content generated by corporate social media channels. For example, some cinemas initiated contests or giveaways surrounding the upcoming release of films. Additionally, our dataset also included retweeted or reposted content of both corporate and user channels. While the large majority of our collected eWOM messages were from individuals this is still an important point because corporate-generated content may be perceived as less credible or trustworthy than user-generated content1 (Kim,

2014; Hennig-Thurau et al., 2014). The different effectiveness of eWOM content dependent on the type of sender (user / company / repost) provides an interesting direction for future research.

The automatic valence coding was convenient, but may have caused bias. For example, films were sometimes described by people as “ziek” (sick) or “gruwelijk”

1 Unfortunately, we do not have exact figures of the ratio between individual and corporate messages.

(gruesome), Dutch slang words for ‘cool’ or ‘nice’. The software sometimes interpreted such words as negative valence. Also, while we studied the effects of eWOM valence, we did not account for the proportion of valence relative to the volume. For example, it could be that the effects of positive or negative messages are different when there is less overall eWOM volume. Additionally, it could also be that when there are a lot of neutral messages, a message that contains a value judgement will have more effect. Lastly, it could be that a higher level of conflicting eWOM valence leads to more sales, like Clement et al. (2007) found for critics in the book industry. It would be interesting to study these topics in future research.

Another limitation of this study is that we used Dutch eWOM data in combination with both domestic and foreign films. The Dutch premiere date formed the boundary between the pre- and post-release periods. However, some foreign films were already released in their domestic market at the time of the Dutch premiere. In combination with the fact that people also download and share films illegally before the official release, this explains that we already found messages containing substantive value judgements prior to the release. A way to cope with this would be to use only Dutch films (Gemser et al., 2007). However, because the Dutch film industry is so small we were unable to get a large enough box office sample of

Dutch films. Going back much further in time would have complicated drawing any conclusions about the effects on box office due to changes in inflation and other economic factors.

It is also useful to note that some films are expected to generate more revenues than others. Producers of films with relatively low production budgets will be happier with lower box office sales than producers of big blockbusters. Furthermore, some - niche - films are not necessarily expected to generate high revenues at all. It would be interesting to study the effects of eWOM on aesthetic success of such films and to also take production budgets into account. Considering the control variable critic ratings, for future research it would be

interesting to include user ratings as can be found on IMDB or Moviemeter, in order to see how the effects of such explicit consumer value judgements compare to that of the often more implicit valence in social media eWOM.

Our method of dividing the sample in three film types, and specifically the fact that we only used three coders, may raise questions as this method was obviously highly sensitive to subjectivity of the coders. To increase the reliability of our proposed method, future studies could include more coders. Even then, there still may be better ways to make the film type distinction. Using an objective source like ‘production budget’ or ‘cinema’ would increase the reliability of a study like this one. However, as we argued in section 2.6, we chose to not do this because we feared that such a method would be at the expense of the internal validity, as the objective sources that were used in earlier studies can no longer be seen as valid dividing variables. Future research should therefore focus on the blurry line between mainstream and niche goods, how to operationalize it in a model and the parameters that determine the boundary between the two.

Lastly, we measured the effects of eWOM on sales on a large scale using quantitative analyses. It would be interesting to gain more insight in the specific factors that drive people to purchase creative goods and what role eWOM plays in that process. A qualitative research design could be very helpful in studying this. Also, while our study provides insights in how eWOM and sales relate to each other in creative industries, it does not say anything about the causality of the found relationships. It would be interesting to use an experimental design to investigate which variables actually influence each other, and therefore unravelling the

‘chicken or the egg’ issue.

Despite the above mentioned limitations, our study contributes to the eWOM literature by providing insight in eWOM effects on experience goods from a different perspective. With using a social media monitor tool to indicate the effects of eWOM on sales we took a step that

not many before us have taken. We showed that when using a large amount of social media messages from multiple sources as a proxy for eWOM, some of the earlier found effects hold while others disappear or change. While a single source data collection method may be useful for a more specific product or service, the fact that experience goods are a frequent subject of discussion between people suggests that scholars should try to capture eWOM of consumers on these goods from sources that represent real-life situations as much as possible. By doing so in this study, we pave the way for future contributions in this field so that both scholars and practitioners can gain more insights in the dynamics between person-to-person interactions and sales in creative industries.

5.3 Practical implications

Our results provide several implications for managers in film industries. First and foremost: no matter what kind of film is released, it is always a good idea to find ways to maximize the volume of eWOM. Possible ways to do this are ‘like & share’ contests in which consumers can for example win free snacks or tickets to a film. Also, cinemas could reserve one timeslot per week in which visitors determine what film is to be played based on social media likes and reposts or retweets. Cinemas could also do more to encourage visitors to share their actual film going behavior on social media. For example, most cinemas already offer consumers the possibility to share their ticket purchase for a film on social media. By offering a reward in return, for instance a discount on a snack or a future visit, consumers may be more likely to share their film visits, which fuels new online discussions with their peers and raises the overall amount of eWOM.

Similar techniques can be used to encourage consumers to post their opinion on a film on social media after a cinema visit. Our results showed that negative messages on social media never hurt the commercial performance of a film, and can even be beneficial if they 56

appear prior to the release, especially when the film is not a typical mainstream or niche film.

This even further emphasizes the importance to maximize the volume of eWOM and it also implies that studios and cinemas should not worry about negative eWOM.

Considering the different types of films, our results showed that the beneficial eWOM effects are the greatest for mainstream films. Producers of mainstream will therefore benefit the most from the following up on the above-described implications. For niche films we found that while the valence of eWOM did not affect box office sales at all, critic ratings were still significant, while this was not the case for mainstream and unclear films. This suggests that when a studio plans to release a niche film, it benefits more from receiving favorable reviews from critics than from receiving positive eWOM on social media.

6. Conclusion

The aim of this study was to analyze to what extent multiple source eWOM related to both early and cumulative sales of experience goods in the form of films. We furthermore asked the question to what extent this effect would be different for mainstream and niche films. We hypothesized that a higher volume and more positive valence of eWOM would lead to more revenues in both periods. Considering the different types of films, we expected that for niche films, the eWOM effects would be stronger on opening weekend box office while for mainstream films the effects would be stronger on cumulative box office. Lastly, we expected that the positive effects of volume would be the greatest for niche films.

Not every hypothesis was confirmed. While the volume of eWOM did prove to be crucial in every situation, its effect was stronger for mainstream films and positive valence proved to be only beneficial in the cumulative period for mainstream and unclear films. For niche films the effects were not different in both periods while our proposed mainstream effect was partially confirmed as positive valence was significant in the cumulative period, 57

and not in the pre-release period. In addition, we found that negative eWOM valence prior to the release of a creative good can be beneficial for sales, especially when a good is perceived as not being typically mainstream or niche.

Overall, our study contributes to the literature on the effects of eWOM on sales in the creative industries and takes it one step further by using an innovative method for both the eWOM data collection and the classification of films. These new methods and insights pave the way for a number of interesting directions for future research on this ever changing topic.

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Appendices

Appendix A: List of films used in final sample

FILM RELEASE OPENING B.O. SCREENS CUMULATIVE B.O. Justin Bieber's Believe 02-01-14 € 48.147 29 € 114.710.00 Paranormal Activity: Marked Ones 02-01-14 € 201.259 44 € 526.513.00 Secret Life of Walter Mitty 02-01-14 € 310.475 90 € 912.865.00 Delivery Man 09-01-14 € 108.034 39 € 459.912.00 Philomena 09-01-14 € 147.083 43 € 1.432.073.00 Wolf of Wall Street 09-01-14 € 943.866 122 € 6.001.055.00 Jack Ryan: Shadow Recruit 16-01-14 € 313.476 82 € 1.004.384.00 Ender's Game 23-01-14 € 83.286 45 € 223.605.00 Dallas Buyers Club 23-01-14 € 50.408 20 € 316.098.00 Devil's Due 23-01-14 € 239.958 42 € 620.346.00 Out of the Furnace 30-01-14 € 64.734 33 € 205.231.00 Selfish Giant 30-01-14 € 31.251 12 € 218.892.00 I, Frankenstein 30-01-14 € 129.990 40 € 337.702.00 Toscaanse bruiloft 30-01-14 € 746.694 128 € 4.496.499.00 Legend of Hercules 06-02-14 € 130.234 30 € 407.874.00 American Hustle 06-02-14 € 271.513 65 € 992.682.00 Cloudy with a Chance of Meatballs 2 06-02-14 € 327.250 151 € 2.195.529.00 K3 Dierenhotel 12-02-14 € 167.528 115 € 1.477.658.00 Lego Movie 12-02-14 € 449.107 210 € 3.295.044.00 African Safari 13-02-14 € 27.983 59 € 111.011.00 RoboCop 13-02-14 € 278.683 70 € 791.670.00 That Awkward Moment 13-02-14 € 265.941 67 € 1.217.282.00 Monuments Men 13-02-14 € 467.252 100 € 1.680.194.00 Lone Survivor 20-02-14 € 35.666 35 € 115.614.00 Winter's Tale 20-02-14 € 60.537 51 € 149.929.00 Vampire Academy 20-02-14 € 79.769 74 € 229.246.00 12 Years a Slave 20-02-14 € 378.908 65 € 3.302.356.00 Mr. Peabody & Sherman 26-02-14 € 273.683 166 € 1.427.473.00 Nebraska 27-02-14 € 64.494 25 € 418.679.00 Best Night Ever 06-03-14 € 80.054 35 € 215.687.00 Ride Along 06-03-14 € 121.611 38 € 491.841.00 Kenau 06-03-14 € 116.507 113 € 553.226.00 Need for Speed 13-03-14 € 320.628 98 € 1.026.022.00 Grand Budapest Hotel 13-03-14 € 223.504 31 € 2.369.678.00 Hartenstraat 13-03-14 € 446.585 123 € 3.155.092.00 Grudge Match 20-03-14 € 32.363 19 € 70.338.00 Endless Love 20-03-14 € 40.952 33 € 156.018.00 August: Osage County 20-03-14 € 90.217 48 € 508.036.00 3 Days to Kill 20-03-14 € 176.632 43 € 607.053.00 Book Thief 27-03-14 € 82.120 51 € 274.085.00 Ida 27-03-14 € 24.429 16 € 305.523.00 Captain America: The Winter Soldier 27-03-14 € 522.884 96 € 1.926.226.00 Snowpiercer 03-04-14 € 44.190 38 € 164.028.00 Lucia de B. 03-04-14 € 177.591 82 € 1.010.746.00 Divergent 03-04-14 € 390.237 92 € 1.924.673.00 Pim & Pom, Het Grote Avontuur 09-04-14 € 40.855 107 € 436.477.00 Rio 2 09-04-14 € 434.680 213 € 4.695.914.00 The Keeper of Lost Causes 10-04-14 € 21.637 17 € 108.928.00 Yves Saint Laurent 10-04-14 € 47.498 35 € 259.672.00 Noah 10-04-14 € 569.773 103 € 2.297.058.00 65

Flits & Het magische huis 16-04-14 € 53.868 92 € 753.269.00 TinkerBell en de piraten 16-04-14 € 61.728 149 € 811.799.00 Weekend in Paris 17-04-14 € 37.812 19 € 333.626.00 Pompeii 17-04-14 € 180.362 75 € 584.727.00 Oculus 17-04-14 € 175.362 44 € 600.077.00 Amazing Spider-Man 2 24-04-14 € 652.784 113 € 2.567.140.00 Other Woman 24-04-14 € 368.748 115 € 2.998.799.00 Reasonable Doubt 01-05-14 € 39.220 13 € 119.957.00 Railway Man 01-05-14 € 45.669 29 € 187.557.00 100 jr. man die uit het raam klom 01-05-14 € 176.561 77 € 1.239.797.00 Anni felici (Those Happy Years) 08-05-14 € 11.918 17 € 82.412.00 Brick Mansions 08-05-14 € 118.646 42 € 399.638.00 Muppets Most Wanted 08-05-14 € 150.355 163 € 561.962.00 Bad Neighbors 08-05-14 € 610.611 80 € 2.667.089.00 Sabotage 15-05-14 € 19.612 12 € 104.997.00 Haunted House 2 15-05-14 € 65.120 39 € 255.645.00 Godzilla 15-05-14 € 694.386 100 € 2.057.422.00 Starred Up 22-05-14 € 26.911 26 € 168.923.00 Walk of Shame 22-05-14 € 69.338 48 € 271.729.00 X-Men: Days of Future Past 22-05-14 € 735.364 111 € 2.657.599.00 Enemy 29-05-14 € 32.551 20 € 101.980.00 Million Ways to Die in the West 29-05-14 € 241.803 75 € 546.853.00 Edge of Tomorrow 29-05-14 € 396.142 116 € 1.196.802.00 Maleficent 29-05-14 € 692.339 118 € 2.640.633.00 22 Jump Street 03-06-14 € 524.168 86 € 3.673.108.00 Grace of Monaco 05-06-14 € 45.981 58 € 250.407.00 City of Violence (Zulu) 12-06-14 € 8.454 21 € 38.239.00 Philosophers (After the Dark) 12-06-14 € 13.358 10 € 84.044.00 Pieter Post de film 18-06-14 € 26.112 70 € 222.794.00 Heksen bestaan niet 18-06-14 € 120.983 120 € 1.554.927.00 Jersey Boys 19-06-14 € 30.564 31 € 101.837.00 Deux jours, une nuit 19-06-14 € 54.958 22 € 364.707.00 Transcendence 19-06-14 € 199.490 59 € 644.589.00 Walking on Sunshine 19-06-14 € 120.339 110 € 716.203.00 Koemba: de zebra 25-06-14 € 100.132 103 € 1.555.636.00 Love Punch 26-06-14 € 34.257 58 € 142.795.00 Two Faces of January 26-06-14 € 30.678 28 € 193.671.00 How to Train Your Dragon 2 02-07-14 € 424.452 211 € 4.341.344.00 Raid 2 03-07-14 € 15.767 13 € 110.641.00 Oorlogsgeheimen 03-07-14 € 97.164 120 € 1.176.804.00 Loenatik, te gek! 09-07-14 € 26.775 54 € 256.458.00 Step Up 5: All In 10-07-14 € 309.788 102 € 1.548.024.00 Fault in Our Stars 10-07-14 € 380.744 66 € 2.896.525.00 Transformers: Age of Extinction 10-07-14 € 676.822 124 € 3.330.676.00 Begin Again 17-07-14 € 30.940 29 € 204.874.00 Fading Gigolo 17-07-14 € 25.090 29 € 232.378.00 Dawn of the Planet of the Apes 17-07-14 € 723.968 108 € 4.069.744.00 Thousand Times Good Night 24-07-14 € 13.728 18 € 118.182.00 Purge: Anarchy 24-07-14 € 160.075 51 € 646.386.00 Blended 24-07-14 € 226.172 69 € 947.233.00 Hanni & Nanni 3 31-07-14 € 23.675 25 € 303.023.00 Hercules 31-07-14 € 335.955 75 € 1.251.338.00 Boyhood 31-07-14 € 148.347 37 € 2.107.201.00 Lucy 31-07-14 € 536.316 82 € 2.688.334.00 And So It Goes 07-08-14 € 44.455 38 € 173.741.00 Expendables 3 14-08-14 € 467.303 106 € 1.486.931.00 Let's Be Cops 14-08-14 € 356.624 53 € 2.741.552.00 66

Guardians of the Galaxy 14-08-14 € 830.265 81 € 3.012.340.00 Magic in the Moonlight 21-08-14 € 154.881 45 € 722.352.00 Ninja Turtles 21-08-14 € 337.960 92 € 1.097.632.00 Maps to the Stars 28-08-14 € 35.653 23 € 128.937.00 Sin City: A Dame to Kill for 28-08-14 € 86.728 53 € 266.457.00 Into the Storm 28-08-14 € 342.189 91 € 1.028.871.00 As Above/So Below 04-09-14 € 122.569 52 € 365.906.00 Most Wanted Man 04-09-14 € 166.410 51 € 991.166.00 Clouds of Sils Maria 11-09-14 € 27.061 30 € 182.021.00 November Man 11-09-14 € 136.084 62 € 402.162.00 Deliver Us from Evil 18-09-14 € 112.502 52 € 378.890.00 If I Stay 18-09-14 € 134.914 41 € 555.681.00 Dorsvloer vol confetti 18-09-14 € 63.766 42 € 565.953.00 Trip to Italy 25-09-14 € 32.429 24 € 173.457.00 Bloedlink 25-09-14 € 95.449 58 € 321.420.00 Winter Sleep (Kis uykusu) 25-09-14 € 41.555 22 € 398.973.00 Pijnstillers 25-09-14 € 129.794 117 € 1.129.128.00 Equalizer 25-09-14 € 460.382 88 € 2.074.285.00 Maze Runner 25-09-14 € 688.654 95 € 4.136.343.00 Boxtrolls 01-10-14 € 148.694 166 € 1.392.465.00 Annabelle 02-10-14 € 139.444 38 € 691.311.00 Wonderbroeders 02-10-14 € 129.507 94 € 873.049.00 Gone Girl 02-10-14 € 353.167 106 € 2.430.562.00 Maya Eerste vlucht 08-10-14 € 64.233 108 € 579.726.00 Sint & Diego Geh.van de Ring 08-10-14 € 29.179 88 € 607.559.00 Locke 09-10-14 € 28.352 19 € 167.748.00 Hundred-Foot Journey 09-10-14 € 95.535 52 € 558.246.00 Dracula Untold 09-10-14 € 337.214 74 € 932.077.00 Dummie de Mummie 09-10-14 € 180.929 121 € 2.504.838.00 Club van Sinterklaas - Prt. Paard 15-10-14 € 72.428 120 € 1.619.438.00 Judge 16-10-14 € 101.373 59 € 331.470.00 Pride 16-10-14 € 46.297 34 € 354.710.00 Walk Among the Tombstones 16-10-14 € 167.286 56 € 674.497.00 Best of Me 16-10-14 € 195.143 84 € 888.169.00 Infiltrant 23-10-14 € 67.421 21 € 197.503.00 St. Vincent 23-10-14 € 59.109 31 € 258.968.00 Fury 23-10-14 € 536.737 91 € 2.039.753.00 Dansen op de vulkaan 30-10-14 € 45.201 64 € 148.587.00 Drop 30-10-14 € 73.376 36 € 236.209.00 Ouija 30-10-14 € 184.904 66 € 541.016.00 Nightcrawler 06-11-14 € 48.500 31 € 186.805.00 Aanmodderfakker 06-11-14 € 74.014 30 € 453.876.00 Pak van mijn hart 06-11-14 € 490.670 125 € 3.539.366.00 Interstellar 06-11-14 € 697.089 106 € 3.978.899.00 Mommy 13-11-14 € 36.317 24 € 246.061.00 Whiplash 13-11-14 € 41.023 22 € 343.423.00 Dumb and Dumber To 13-11-14 € 428.243 99 € 1.530.743.00 Samba 13-11-14 € 227.596 57 € 1.919.036.00 Wiplala 19-11-14 € 114.349 124 € 1.873.119.00 Hunger Games: Mockingjay - Part 1 19-11-14 € 1.547.332 118 € 6.415.142.00 John Wick 20-11-14 € 238.749 65 € 1.212.803.00 Jessabelle 27-11-14 € 48.224 26 € 170.090.00 Trash 27-11-14 € 32.838 40 € 173.559.00 My Old Lady 27-11-14 € 50.488 27 € 591.752.00 Horrible Bosses 2 27-11-14 € 293.897 100 € 1.080.113.00 Penguins of Madagascar 03-12-14 € 278.022 226 € 3.718.504.00 Mees Kees op de planken 03-12-14 € 188.533 125 € 4.044.478.00 67

Salt of the Earth 04-12-14 € 19.730 18 € 200.924.00 Gooische vrouwen 2 04-12-14 € 1.783.615 152 € 16.883.928.00 Hobbit: The Battle of the Five Armies 10-12-14 € 1.848.828 226 € 12.042.569.00 Het nieuwe Rijksmuseum 11-12-14 € 13.581 32 € 104.717.00 Two Night Stand 11-12-14 € 35.924 15 € 232.941.00 Mr. Turner 11-12-14 € 73.210 26 € 741.255.00 Asterix en Obelix 3D 17-12-14 € 37.141 55 € 539.083.00 You're Not You 18-12-14 € 46.643 43 € 344.036.00 Love, Rosie 18-12-14 € 65.508 51 € 495.999.00 Tinkerbell & het nooitgedachtbeest 18-12-14 € 32.917 96 € 508.617.00 Exodus: Gods and Kings 18-12-14 € 448.984 101 € 2.895.363.00 Love Is Strange 08-01-15 € 32.167 20 € 94.302.00 Unbroken 08-01-15 € 316.479 78 € 970.992.00 Imitation Game 08-01-15 € 329.432 53 € 2.451.387.00 Onder het hart 15-01-15 € 39.258 34 € 82.542.00 Theory of Everything 15-01-15 € 168.954 57 € 892.398.00 Taken 3 15-01-15 € 741.065 86 € 2.475.397.00 capitale umano 22-01-15 € 32.170 21 € 132.106.00 Blackhat 22-01-15 € 156.229 65 € 355.212.00 Birdman 22-01-15 € 169.803 32 € 1.071.049.00 Homies 22-01-15 € 369.111 101 € 1.622.320.00 SpongeBob: Spons op het droge 28-01-15 € 524.853 168 € 2.702.512.00 Interview 29-01-15 € 28.501 34 € 49.959.00 Taking of Deborah Logan 29-01-15 € 39.713 20 € 67.230.00 Into the Woods 29-01-15 € 112.062 40 € 230.949.00 Michiel de Ruyter 29-01-15 € 827.933 133 € 5.896.503.00 Jack bestelt een broertje 04-02-15 € 76.585 118 € 722.008.00 Big Eyes 05-02-15 € 35.675 27 € 55.341.00 Inherent Vice 05-02-15 € 73.127 30 € 179.775.00 Wild Card 05-02-15 € 99.294 35 € 253.799.00 Jupiter Ascending 05-02-15 € 360.784 72 € 1.027.589.00 Night at the Museum: Secret Tomb 05-02-15 € 245.835 95 € 1.203.020.00 Paddington 11-02-15 € 163.832 162 € 1.771.673.00 Big Hero 6 11-02-15 € 223.206 183 € 2.570.249.00 Most Violent Year 12-02-15 € 34.677 25 € 57.835.00 Woman in Black: Angel of Death 12-02-15 € 61.606 37 € 186.600.00 Kingsman: The Secret Service 12-02-15 € 451.168 101 € 2.510.435.00 Fifty Shades of Grey 12-02-15 € 1.474.192 122 € 6.388.543.00 Mortdecai 19-02-15 € 77.155 39 € 138.277.00 Turist (Force Majeure) 19-02-15 € 63.661 29 € 273.678.00 Boy 7 19-02-15 € 113.908 54 € 364.732.00 Selma 19-02-15 € 119.137 44 € 652.045.00 Seventh Son 26-02-15 € 247.172 67 € 502.847.00 Chappie 05-03-15 € 155.332 73 € 373.280.00 American Sniper 05-03-15 € 551.710 77 € 2.464.125.00 Cobbler 12-03-15 € 34.323 20 € 43.537.00 Boy Next Door 12-03-15 € 154.911 41 € 563.918.00 Still Alice 12-03-15 € 151.675 35 € 1.433.443.00 Cinderella 18-03-15 € 259.243 180 € 1.297.128.00 Divergent Series: Insurgent 18-03-15 € 603.279 107 € 2.320.139.00 Le meraviglie 19-03-15 € 28.748 18 € 88.544.00 Second Best Exotic Marigold Hotel 19-03-15 € 103.673 41 € 420.746.00 Shaun het schaap 25-03-15 € 241.021 131 € 1.402.840.00 Suite Française 26-03-15 € 53.202 42 € 141.559.00 Timbuktu 26-03-15 € 31.801 15 € 232.669.00 Gunman 26-03-15 € 164.095 78 € 365.163.00 Bloed, zweet & tranen 02-04-15 € 666.553 130 € 2.559.244.00 68

Fast & Furious 7 02-04-15 € 2.301.183 114 € 8.400.655.00 Son of a Gun 09-04-15 € 30.280 18 € 65.064.00 Longest Ride 09-04-15 € 222.242 68 € 1.239.711.00 De notenkraak (The Nut Job) 15-04-15 € 24.478 83 € 366.558.00 Water Diviner 16-04-15 € 55.611 42 € 130.701.00 Run All Night 16-04-15 € 169.823 68 € 551.113.00 famille Bélier (The Bélier Family) 16-04-15 € 99.721 38 € 938.243.00 Kidnep 22-04-15 € 44.418 79 € 423.127.00 Avengers: Age of Ultron 22-04-15 € 1.299.078 116 € 4.915.584.00 It Follows 23-04-15 € 60.312 22 € 181.239.00 Boskampi's 29-04-15 € 183.309 124 € 1.158.786.00 Im Labyrinth des Schweigens 30-04-15 € 41.201 29 € 72.014.00 Ex_Machina 30-04-15 € 38.500 8 € 89.727.00 Tracers 30-04-15 € 59.055 35 € 119.224.00 De ontsnapping 30-04-15 € 351.923 120 € 2.265.534.00 Child 44 07-05-15 € 33.073 23 € 93.639.00 Dark Horse 07-05-15 € 32.329 27 € 135.121.00 Far from the Madding Crowd 07-05-15 € 49.323 32 € 216.762.00 Big Game 07-05-15 € 82.843 37 € 280.661.00 Get Hard 07-05-15 € 174.981 51 € 585.377.00 Ventoux 14-05-15 € 214.938 106 € 747.285.00 Mad Max: Fury Road 14-05-15 € 557.299 106 € 2.022.606.00 Pitch Perfect 2 14-05-15 € 597.347 69 € 2.102.632.00 Masters 21-05-15 € 73.711 79 € 177.368.00 Poltergeist 21-05-15 € 167.279 40 € 444.573.00 Project T 21-05-15 € 268.331 94 € 719.558.00 De surprise 21-05-15 € 181.785 81 € 743.068.00 Schneider vs. Bax 28-05-15 € 69.615 38 € 236.420.00 Woman in Gold 28-05-15 € 65.813 34 € 310.013.00 San Andreas 28-05-15 € 560.853 99 € 1.594.723.00 Man Up 04-06-15 € 9.471 11 € 32.660.00 Unfriended 04-06-15 € 84.918 50 € 316.585.00 Rendez-vous 04-06-15 € 124.395 119 € 977.998.00 Spy 04-06-15 € 337.025 97 € 1.920.792.00 Apenstreken 10-06-15 € 77.559 115 € 624.824.00 Taxi Teheran 11-06-15 € 31.172 24 € 151.299.00 Entourage 11-06-15 € 122.541 64 € 395.947.00 Jurassic World 11-06-15 € 1.904.123 123 € 9.950.991.00 Blade Runner, The Final Cut 18-06-15 € 25.940 16 € 38.923.00 King's Gardens 18-06-15 € 31.558 32 € 87.850.00 Age of Adaline 18-06-15 € 186.980 98 € 979.065.00 Spangas in actie 18-06-15 € 172.294 113 € 1.040.311.00 Code M 24-06-15 € 16.480 103 € 110.526.00 Kidnapping Freddy Heineken 25-06-15 € 21.726 61 € 46.128.00 Nature 25-06-15 € 46.946 85 € 305.993.00 Insidious: Chapter 3 25-06-15 € 166.034 56 € 614.131.00 Minions 01-07-15 € 1.035.573 132 € 13.079.127.00 tête haute 02-07-15 € 8.614 21 € 50.143.00 Survivor 02-07-15 € 31.918 27 € 132.331.00 Ruth & Alex 02-07-15 € 14.236 27 € 133.421.00 Magic Mike XXL 02-07-15 € 248.788 117 € 2.609.780.00 Dior and I 09-07-15 € 12.813 13 € 52.770.00 La isla mínima 09-07-15 € 25.648 16 € 199.982.00 Terminator: Genisys 09-07-15 € 475.029 95 € 1.579.981.00 Beestenboot (Two by Two) 15-07-15 € 16.880 70 € 219.723.00 Inside Out (Binnenstebuiten) 15-07-15 € 515.427 304 € 5.213.769.00 Gallows 16-07-15 € 49.822 28 € 166.924.00 69

Mr. Holmes 16-07-15 € 51.108 32 € 291.647.00 Self/less 16-07-15 € 89.309 46 € 353.472.00 De reünie 16-07-15 € 122.263 83 € 687.720.00 Mita tova (The Farewell Party) 23-07-15 € 22.069 22 € 65.452.00 Ant-Man 23-07-15 € 569.973 91 € 1.658.811.00 De kleine prins 29-07-15 € 26.357 73 € 276.262.00 loi du marché 30-07-15 € 15.981 18 € 35.895.00 While We're Young 30-07-15 € 44.565 31 € 201.210.00 Paper Towns 30-07-15 € 233.763 85 € 1.193.527.00 Mission: Impossible Rogue Nation 30-07-15 € 818.235 121 € 3.854.490.00 Dark Places 06-08-15 € 37.995 34 € 170.205.00 Fantastic Four 06-08-15 € 245.083 83 € 653.618.00 Amy 13-08-15 € 222.254 60 € 1.505.949.00 Sinister 2 20-08-15 € 106.660 37 € 392.228.00 Irrational Man 20-08-15 € 57.970 45 € 432.488.00 Man from U.N.C.L.E. 20-08-15 € 170.653 103 € 718.158.00 Pixels 20-08-15 € 279.012 102 € 1.055.540.00 Victoria 27-08-15 € 14.079 21 € 22.712.00 X+Y 27-08-15 € 18.057 26 € 32.023.00 American Ultra 27-08-15 € 79.835 60 € 182.735.00 We Are Your Friends 27-08-15 € 116.873 79 € 308.573.00 Trainwreck 27-08-15 € 123.309 65 € 402.957.00 Knock Knock 03-09-15 € 37.911 23 € 74.428.00 Ricki and the Flash 03-09-15 € 40.288 36 € 105.672.00 Dheepan 03-09-15 € 40.112 40 € 149.664.00 Mia madre (My Mother) 03-09-15 € 55.125 29 € 213.431.00 Hitman: Agent 47 03-09-15 € 229.854 55 € 655.489.00 Straight Outta Compton 03-09-15 € 264.273 49 € 862.556.00 Southpaw 10-09-15 € 65.435 25 € 267.431.00 Visit 10-09-15 € 105.249 58 € 367.175.00 Vacation 10-09-15 € 238.351 99 € 809.316.00 Schone handen 10-09-15 € 200.156 111 € 1.062.147.00 Knight of Cups 17-09-15 € 23.225 20 € 34.582.00 Everest 17-09-15 € 340.557 86 € 1.261.980.00 Maze Runner: The Scorch Trials 17-09-15 € 945.395 160 € 3.754.565.00 Transporter Refueled 24-09-15 € 144.301 64 € 391.286.00 Holland, natuur in de delta 24-09-15 € 114.056 150 € 672.786.00 45 Years 24-09-15 € 112.815 39 € 1.115.617.00 Intern 24-09-15 € 267.457 88 € 1.548.839.00 J. Kessels 01-10-15 € 21.210 32 € 80.622.00 Martian 01-10-15 € 626.050 129 € 3.481.580.00 Keet&Koen - Bassie&Adriaan 07-10-15 € 74.313 119 € 743.489.00 Pan 07-10-15 € 164.709 228 € 1.031.073.00 Hotel Transylvania 2 07-10-15 € 264.388 127 € 3.052.038.00 Walk 08-10-15 € 103.208 81 € 282.694.00 Sicario 08-10-15 € 147.790 55 € 549.043.00 Popoz 08-10-15 € 156.616 87 € 761.401.00 Club van Sinterklaas Verdw. Schoentjes 14-10-15 € 56.271 117 € 1.592.942.00 Crimson Peak 15-10-15 € 197.158 64 € 526.390.00 Black Mass 15-10-15 € 203.310 64 € 756.066.00 Ja, ik wil! 15-10-15 € 369.122 124 € 3.034.504.00 Last Witch Hunter 22-10-15 € 260.769 51 € 791.837.00 Paranormal Activity: Ghost Dimension 22-10-15 € 296.925 50 € 960.174.00 No Escape 29-10-15 € 40.643 23 € 219.893.00 giovinezza (Youth) 29-10-15 € 127.567 36 € 1.327.351.00 Spectre 29-10-15 € 3.377.404 132 € 20.427.555.00 Gift 05-11-15 € 49.111 23 € 171.283.00 70

Sleeping with Other People 05-11-15 € 61.479 36 € 191.720.00 Le tout nouveau testament 05-11-15 € 51.873 26 € 292.627.00 Saul fia (Son of Saul) 05-11-15 € 62.955 27 € 344.880.00 Scouts Guide to the Zombie Apocalypse 12-11-15 € 33.810 50 € 66.652.00 Regression 12-11-15 € 80.361 40 € 225.150.00 Hunger Games: Mockingjay - Part 2 18-11-15 € 1.656.539 132 € 7.331.747.00 Program 19-11-15 € 21.138 29 € 54.230.00 Family Affair 19-11-15 € 22.555 14 € 68.561.00 Good Dinosaur 25-11-15 € 333.230 153 € 3.431.710.00 Solace 26-11-15 € 29.626 15 € 102.916.00 Burnt 26-11-15 € 126.988 66 € 690.204.00 Er ist wieder da (Look Who's Back) 26-11-15 € 145.791 63 € 970.478.00 Bridge of Spies 26-11-15 € 259.868 77 € 1.464.988.00 Fashion Chicks 02-12-15 € 125.865 95 € 1.375.385.00 Pawn Sacrifice 03-12-15 € 16.167 20 € 40.683.00 Hallo Bungalow 03-12-15 € 72.022 75 € 182.835.00 Steve Jobs 03-12-15 € 78.622 49 € 199.874.00 Night Before 03-12-15 € 51.781 25 € 240.141.00 Suffragette 03-12-15 € 58.548 32 € 314.977.00 Dummie de Mummie Sfinx Shakaba 09-12-15 € 100.591 130 € 1.679.280.00 Snoopy Charlie Brown: Peanuts Film 09-12-15 € 95.414 197 € 1.730.632.00 Krampus 10-12-15 € 70.497 41 € 261.813.00 In the Heart of the Sea 10-12-15 € 166.389 117 € 531.319.00 Publieke werken 10-12-15 € 156.430 66 € 1.629.830.00 Bon Bini Holland 10-12-15 € 577.716 97 € 4.558.627.00 Star Wars: The Force Awakens 16-12-15 € 2.420.122 190 € 14.268.696.00 Carol 17-12-15 € 104.858 39 € 905.956.00 Mannenharten 2 17-12-15 € 373.091 130 € 3.009.921.00

Appendix B: Instructions for Coders

Dear [NAME CODER],

Hereby I send you the form containing all films released in Dutch cinema in 2014 and 2015. Please code every film in the empty column as follows:

1 = mainstream 2 = niche 3 = unclear / don’t know

Please code 1 if you feel that the film was meant for average, mainstream moviegoers and most related to a blockbuster. Please code 2 if you feel that the film was meant for a specialized target group who are interested in films that offer more quality and depth and most related to arthouse. Please code 3 if you do not know which category a film belongs to or if you feel that the film does not belong in either of the two categories.

Please determine your code based on the overall appeal the film has on you and the way you think it was positioned in the market. Next to every film title you will find a link to the IMDB page of that particular film, which you are free to visit to refresh your memory.

If you have any questions, please let me know.

Thank you!

Appendix C: Coosto output for ‘Maleficent’

Appendix C: Pre-release

Appendix C: Post-release