DETERMINANTS of the BOX OFFICE SUCCESS in the EUROPEAN FILM MARKET Faktory Finančního Úspěchu Filmu Na Evropském Filmovém Trhu

Masaryk University Faculty of Economics and Administration Department of Finance

DETERMINANTS OF THE BOX OFFICE SUCCESS IN THE EUROPEAN FILM MARKET Faktory finančního úspěchu filmu na evropském filmovém trhu

Master thesis

Supervisor: Author: Ing. Daniel NĚMEC, PhD. Mgr. Bc. Adéla DVOŘÁKOVÁ

Brno, 2017

MASARYKOVA UNIVERZITA Ekonomicko-správní fakulta

ZADÁNÍ DIPLOMOVÉ PRÁCE

Akademický rok: 2016/2017

Studentka: Mgr. Bc. Adéla Dvořáková

Obor: Finance

Název práce: Faktory finančního úspěchu filmu na evropském filmovém trhu

Název práce anglicky: Determinants of the Box Ofﬁce Success in the European Film Market

Cíl práce, postup a použité metody: Předmětem práce je identifikace faktorů, které mají vliv na tržby filmů na evrop- ském filmovém trhu a odhad velikosti tohoto vlivu, formulace závěrů ohledně možností producentů ovlivnit tržby jejich filmu, který lze chápat jako rizikovou investici. Práce se tedy bude zaměřovat na film jakožto projekt spojený s vysokou mírou nejistoty a rizika. Náplní práce bude zanalyzovat vzorek filmů vypro- dukovaných na evropském filmovém trhu a identifikovat faktory, které ovlivňují ekonomický úspěch filmu, s důrazem na ty, které mohou být ovlivněny již pro- ducenty v prvotních fázích projektu (pre-release factors). Výstupem by měly být závěry ohledně možnosti producenta ovlivnit tržby filmu a případná doporučení v tomto ohledu.

Použitá data: databáze IMDb.com (International Movie Database)

Metody: studium literatury a předchozích výzkumů na dané téma, regresní ana- lýza, syntéza

Postup práce:

1. Vymezení cíle práce a pracovních hypotéz, charakteristika filmového trhu a jeho specifik (bariéry vstupu na trh, produkce filmu, finanční aspekty produkce filmu, důraz na riziko pro investory).

2. Rešerše předchozích výzkumů na dané téma se zaměřením na přístupy a metody v nich užité.

4. Představení zdrojů dat a datové báze užité v práci, představení použitých eko- nometrických nástrojů, formulace ekonometrického modelu a jeho věcný popis, odhad parametrů, provedení diagnostických testů, ověření robustnosti výsledků.

5. Prezentace závěrů (interpretace dosažených výsledků), formulace doporučení, shrnutí hlavních výsledků, veriﬁkace v úvodu stanovených hypotéz, porovnání výsledků s jinými studiemi na dané téma.

Rozsah graﬁckých prací: Podle pokynů vedoucího práce

Rozsah práce bez příloh: 60 – 80 stran

Strana 1 z 2 Literatura: KOOP,Gary. Introduction to econometrics. Chichester: John Wiley & Sons, 2008. 371 s. ISBN 9780470032701. FINNEY, Angus a Eugenio TRIANA. The International Film Business: A market guide beyond Hollywood.. 2nd. New York: Routledge, 2015. 287 s. ISBN 978- 0-415-53153-5. TERRY, Neil, John W. COOLEY a Miles ZACHARY. The determinants of foreign box ofﬁce revenue for English language movies.. Journal of International Busi- ness and Cultural Studies, 2010, roč. 2, č. 1, s. 1-12. ISSN 1941-5087. DENIZ, Borga a Robert B. HASBROUCK. WHEN TO GREENLIGHT: Examining the Pre-release Factors that Determine Future Box Ofﬁce Success of a Movie in the United States. International Journal of Economics and Management Sciences, 2012, roč. 2, č. 3, s. 35-42. ISSN 2162-6359.

Vedoucí práce: Ing. Daniel Němec, Ph.D.

Pracoviště vedoucího práce: Katedra ekonomie

Datum zadání práce: 11. 6. 2016

Termín odevzdání diplomové práce a vložení do IS je uveden v platném harmonogramu akademického roku.

V Brně dne: 10. 5. 2017

Strana 2 z 2 Jméno a příjmení autora: Adéla Dvořáková

Název diplomové práce: Faktory finančního úspěchu filmu na evropském filmovém trhu

Název práce v angličtině: Determinants of the Box Office Success in the European Film Market

Katedra: financí

Vedoucí diplomové práce: Ing. Daniel Němec, Ph.D.

Rok obhajoby: 2017

Anotace

Předmětem diplomové práce „Faktory finančního úspěchu filmu na evropském filmovém trhu” je film, jakožto podnikatelský záměr spojený s vysokou mírou rizika. Jejím cílem je identifikace faktorů ovlivňující filmové tržby za pomoci analýzy veřejně dostupných dat a ověření hypotéz týkajících se možných strategií sloužících ke snížení podnikatelského rizika. Úvodní část obsahuje charakteristiku současného evropského filmového trhu a shrnutí dosavadních poznatků. Následuje popis datové báze a analýza dat. V poslední části práce jsou prezentovány výsledky analýzy, shrnuty závěry a formulována doporučení.

Annotation

The subject of the thesis: “Determinants of the Box Office Success in the European Film Market” is a film – a project connected to a high level of business risk. Its goal is to identify the factors which influence the theatrical revenues using publicly available data and to test hypotheses on possible risk-mitigation strategies. The first part, containing characteristics of the contemporary European film market and literature review, is followed by the description of the data set and data analysis. At the end the results are presented and summarized and recommendations are formulated.

Klíčová slova

Film, filmový trh, tržby, riziko, koprodukce

Keywords

Film, film market, box office, risk, co-production

„Prohlašuji, že jsem diplomovou práci vypracovala samostatně pod vedením Ing. Daniela Němce, PhD. a uvedla v ní všechny použité literární a jiné odborné zdroje v souladu s právními předpisy, vnitřními předpisy Masarykovy univerzity a vnitřními akty řízení Masarykovy univerzity a Ekonomicko-správní fakulty MU.“

Brno, 11. 5. 2017

Acknowledgments

I would like to express my sincere gratitude to my advisor, Daniel Němec, for his helpfulness and responsiveness, useful advice and kind support.

My thanks also go to Michał Kazimierczak for his initial ideas, help with the data and suggestions, but most importantly, for being such a good example to me, both professionally and personally.

A big thank you goes also to Cristina Rujan for her advice, comments, technical assistance and proofreading, but most of all, for her enthusiasm and friendship.

Last but not least, I would like to thank my family for having supported me during the whole period of my studies and Luis for being here for me under all circumstances. Table of Contents

1 Introduction ...... 9

2 Risk, revenues and profits in the film industry ...... 11

2.1 Risks associated with the film production ...... 11

2.1.1 Completion, delivery and costs escalation risk ...... 12

2.1.2 Distribution risk ...... 12

2.1.3 Marketing risk ...... 12

2.1.4 Performance risk ...... 13

2.2 Risk mitigation strategies ...... 13

3 Specifics of the market of the European Union ...... 16

3.1 Major operators ...... 16

3.2 Budgets and profits in the EU film industry ...... 16

3.3 Ambivalence of the EU market – single market vs territoriality ...... 17

3.4 Challenges of the EU film industry ...... 18

4 Literature review ...... 19

4.1 Measures of financial success ...... 20

4.2 Methodology ...... 20

4.2.1 Linear regression ...... 20

4.2.2 Path analysis ...... 21

4.2.3 Three-stage least squares ...... 22

4.2.4 Alternative techniques – modelling the probability mass ...... 22

4.2.5 Alternative techniques – artificial neural networks ...... 23

4.3 Factors of success ...... 23

4.3.1 Pre-release factors ...... 23

4.3.2 Post-release factors ...... 30

5 Hypotheses ...... 34

6 Data source: The Internet Movie Database ...... 36

6.1 Qualitative data on films ...... 36

6.2 Financial data on films ...... 37

6.3 Additional data sources ...... 37 7 Description of the data ...... 38

7.1 Financial data on films ...... 38

7.1.1 Box office ...... 38

7.1.2 Profitability ...... 39

7.1.3 Production budget ...... 39

7.2 Qualitative data on films ...... 39

7.2.1 Film genres ...... 39

7.2.2 Use of underlying material ...... 40

7.2.3 Quality indicators – awards and user rating ...... 40

7.2.4 Information on producers – co-productions and major studios ...... 41

7.2.5 Director ...... 42

7.2.6 Production and release time ...... 43

8 Data limitations ...... 44

9 Descriptive statistics ...... 45

10 Method ...... 52

11 Data analysis ...... 53

11.1 Models and results ...... 54

11.1.1 Regression analysis: full sample ...... 54

11.1.2 Regression analysis: EU sample ...... 57

12 Discussion and recommendations...... 63

13 Concluding remarks ...... 65

14 List of references ...... 66

15 List of Shortcuts ...... 71

16 List of graphs and tables ...... 71

17 List of Appendices ...... 71 1 Introduction

The film industry is a multi-billion-dollar business with big economic and cultural impact. The European film industry encompasses over 75 000 companies, employing more than 370 000 people and reaping around 60 billion euros in revenue.1 (European Parliament 2014, p. 1)

The film industry has its specific character, which makes film making a very risky activity. Barriers of entry in the film market are fairly low – practically anyone can become a film producer. However, the fix costs of movie production, distribution and marketing are high, the reproduction costs very low, the demand is fluctuating and highly unpredictable and there is no relation between the quality of the film and the price of a ticket. (European Parliament 2014, p. 2) This combination of factors contributes to the extremely long period of time before a movie starts making money and creates an impediment making it impossible for many of them to break-through. On the other hand, as the marginal costs of an additional user viewing the film or making an additional DVD copy are minimal, a film which manages to cover its production, distribution and marketing costs can become highly-profitable very soon after reaching the break-even point. (Finney 2014, p. 3-26)

The movie industry can be described as a cultural industry, meaning that it is characterized by two specific features: first, there is a persistent oversupply of creative labour, i.e. there are many more aspiring artists than the respective market can support. Second, there is extreme uncertainty regarding the success of a product. (Peltoniemi 2015, p. 42) This uncertainty surrounding the film market has been expressed by the screenwriter William Goldman (1984. p. 39) in his famous statement: “Nobody knows anything,” he writes in Adventures in the Screen Trade. “Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess.”

The film market in general is structured horizontally, on the territorial basis, but also vertically, based on the media which is used to deliver the film to the end-users. The vertical structure can be represented by the film exploitation chain: theatrical release, DVD release, video-on-demand, pay-TV and free TV. This vertical structure enables the film-makers to approach different types of audience and gain income from different sources. The exploitation

1 Data for 2010 9 chain allows for price discrimination – the highest price is paid by the audience which views the film in the cinema, a lower price by those who buy a DVD. To maximize revenues, the studios release the film onto the next form only after the exploitation in the previous one has been exhausted. (Finney 2014, p. 15-26) This thesis is focused on the first stage of the film exploitation chain – theatrical release. The reason is mainly the lack of complete data on the overall revenues of films. However, this fact should not create any obstacle in terms of interpretation of the results, as the theatrical success is a prerequisite of the overall success of a motion picture. If the product fails to address the audience in cinemas, it is very unlikely to make up for that in the later stage of the exploitation chain.

The aim of this thesis is to examine a sample of 1457 films produced and released in the market of the European Union between the years 2000 and 2010 and, using the information available in the Internet Movie Database, identify which features appear to contribute to their financial success. Special attention is paid to those factors which can be, at least partially, influenced by the producer, as changing the properties of the product can be a possible strategy mitigating the business risk related to the development of the project. The film genre, adapted content, co-production, release pattern and employing a well-known director are factors which were identified as possible determinants of the movie’s financial success. Therefore, the partial hypotheses are related to the effect of these factors on the films’ theatrical revenues.

The research related to this topic has widely focused on the market of the United States since the contemporary film market is dominated by the Hollywood studios. To the best of my knowledge, there is no comprehensive study examining the film market of the European Union and the factors influencing success of a film produced by EU studios. This thesis thus aspires to be novel due to its geographical scope.

The thesis consists of a theoretical part, where the risks associated with film production and specifics of the European film market are described and previous research on the topic is presented. The following sections contain the practical part which introduces the empirical analysis with a description of the data sources and data set, regression analysis and presentation and discussion of results.

2 Risk, revenues and profits in the film industry

Films are complex projects and their success depends on a compound of factors which by themselves are hard to predict or influence. Films are unique products, their shelf life is only a few weeks, they enter and exit the market on a continuing basis, they compete against a changing cast of competitors, most of them have only a week or two to capture the audience's attention and the weekly box-office revenues are usually concentrated on only a handful of top ranking films. (De Vany and Walls 1999, p. 288) Moreover, films surely have the nature of “experiential goods”,2 i.e. they are evaluated based on their ability to offer fun, enjoyment and pleasure – very subjective categories – and, prior to consumption, customers cannot know whether the goods will succeed in delivering these. (Peltoniemi 2015, p. 42)

The numbers published by the Motion Picture Association of America can serve as an illustration of the business risk the production companies must face. The average revenue of a motion picture in the United States in 2012 was $16 million. In 2007 (the last year when MPAA published full production costs), the average production cost was $70.8 million and the average advertising cost was $35.9 million – about 50% of production costs. (Escoffier and McKelvey 2015, p. 53) The difference between the total average cost of $107 million and the average revenue is immense. Moreover, the authors usually agree on the skewness of the distribution of movie box-office performances.3 The movie industry is characterized by large revenues in absolute numbers, (Teti 2013, p. 739) but these are concentrated in hands of a few “blockbusters”. According to De Vany and Walls (2002, p. 5) most of the films lose money, which clearly makes movies a “winner-takes-all” business. (De Vany and Walls 2002, p. 5)

2.1 Risks associated with the film production

From the initial idea to the actual performance in the movie theatres, a film goes through a lengthy process and faces a cascade of risks originating in the different stages of the process, from the pre-production phase to the actual showing of the film to the audience. In general, small independent studios have a significantly worse position in the film market. The major film studios have usually more experience and their financial power allows them to

2 “Experiential“ or “experience“ goods are defined as goods which are used to engage an individual in a memorable way. The buyer does not pay for the good itself but rather for the personal experience it offers and the sensations it creates within himself. Thus, no two people can have the same experience, since it only exists in the individual’s mind. (Pine & Gilmore 2011, p.99) 3 See for example De Vany and Walls 1999, Hadida 2009, Teti 2013 11 produce films with higher budgets, spend more money on marketing and control the distribution channels. On the other hand, since they usually produce movies with significantly higher production budgets, big studios have got to reach much higher revenues to gain profit. If a high-budget movie is unprofitable, the loss is often counted in millions.

2.1.1 Completion, delivery and costs escalation risk

The film development, as the early stage of production, can be described as all the work that surrounds the initial concept of the story or idea. It involves seeking for original ideas or secondary source material (books, plays, previous films etc.), screenwriting and raising of development finance. It implies considerable costs, which are unlikely to be returned quickly or, in many cases, not at all. In Europe, it is usually around one out of five or six films which make their way from development to production. In Hollywood, it is roughly one out of twenty. (Finney 2014, p. 83-101)

During the actual film production, additional costs may occur, which might require additional fundraising. As the investment in a film is usually returned after a very long time, if ever, the additional costs escalation can even become an impediment to finishing the production at all. Moreover, with developing technologies, paradoxically, it has become even more expensive to produce a film. (Finney 2014, p. 83-101)

2.1.2 Distribution risk

The small independent studios suffer especially in the distribution phase of the film- making. The major film studios usually have better access to the distribution channels, bigger influence and stronger position in negotiation. In addition, they often use the strategy of vertical integration, meaning that they concentrate the control not only over the film production, but also over distribution, in hands of a single entity. Another threat with respect to distribution is the digital technology, which makes it increasingly difficult for the film- makers to gain profit using the traditional distribution channels. The audience shifted towards renting rather than buying films post theatrical release. (Finney 2014, p. 83-101)

2.1.3 Marketing risk

The short time a film usually has to recoup the investment gives much importance to marketing. The shelf life of a film in the primary market is very short, usually not more than 10 weeks, while, given that the price of a ticket is the same for all films, there is no possibility of price-discrimination. The film has thus a very short time to gain sufficient revenues to 12 make a meaningful profit, which may prove impossible with an unsuitable marketing strategy. (Finney 2014, p. 83-101)

2.1.4 Performance risk

The film industry relies on a constant supply of prototypical productions, none of which are in themselves repeatable. There is a high level of information asymmetry in the market and reactions of the audience are highly unpredictable. Also, the social networks and fan sites ensure that not only good reviews, but also bad reviews spread quickly. (Finney 2014, 3-14) The information cascades among the film-goers can evolve during the film's run along so many paths that it is impossible to attribute the success of a movie to individual causal factors. (De Vany and Walls 1999, p. 285) Therefore, even if a movie overcomes all the obstacles and makes its way to the screens, the eventual success depends on the highly uncertain public acceptance.

2.2 Risk mitigation strategies

Film-makers use different strategies to mitigate risk. Especially big studios take risk management seriously and use a wide range of instruments to decrease it to the lowest possible level.

De Vany and Walls (2002, p. 22) suggest that risk must be managed through the stock market in the pricing and ownership of shares in motion picture companies, with the objective to merge motion picture companies into media conglomerates. These conglomerates function on the basis of vertical and horizontal integration – they usually execute production as well as marketing and distribution, and target different territories and audiences. This strategy is apparently widely used, given that Hollywood has been dominated by six major studios since mid-1930s. (Finney 2014, p. 6) The European film market seems to show similar features. (European Commission 2012, p. 30-31)

Simultaneous production of films with different costs of production has also proved to be a successful strategy as the failure of one project can be healed by the success of another. Teti (2013, p. 739) bases his suggestions on his findings that the box-office earnings are determined by the production costs, while the association between costs and rate of return is completely random. He argues that the huge amount of money invested in production costs could compromise the companies financially if films were managed as individual projects.

Therefore, management decisions focused on the concurrent production of films with different costs would constitute the scheme by which studios could obtain considerable incomes and substantially diversify the risk associated with movie production. A similar conclusion was reached by De Vany and Walls (1999, p. 315): Predicting outcomes for individual movies is so difficult, that a strategy of choosing portfolios of movies may be preferred to the current practice of “greenlighting” individual projects. Small independent entities, however, have effectively no possibility to mitigate the risk in this way.

Especially in the independent film industry, there is a pressing need to find partners to raise money, spread risk and ensure the widest possible distribution of the product. For this reason, producers often seek for partners to co-produce their film. An international co- production can bring additional benefits since the movie is then treated as a national film in the countries of all the participating producers, which allows the film to reach the national support or other financing mechanisms in each territory. (Finney 2014, p. 102-110) Furthermore, the major studios often bear the risks of development, production and distribution, but might slightly decrease the risk level by a symbiotic relation with the independent sector, upon which they rely for creative and commercial innovation, cost reduction, identification of new talent and connection with evolving consumer tastes. Studios can also use different forms of off-balance sheet financing, attracting third parties to share the risk of production and distribution investment. (Finney 2014, p. 4-14)

With respect to the completion risk, the so-called completion guarantee can be put in place. The completion guarantee hands authority to an independent party once the budget is exhausted (or exceeded by a specified amount) and this party's responsibility is to finish the film. (De Vany and Walls 2002, p. 21) However, this type of arrangement secures only the movie completion; the risk that the final product may not be artistically or financially successful becomes even larger.

In the film release stage, co-financiers of big-budget films reduce the risk of a failed opening by avoiding direct competition with other potential blockbusters in their respective portfolios. (Hadida 2009, p. 315) Similarly, strategies might be adopted to adjust the timing of the release to avoid competition with films released by other big studios at that time or to take advantage of increased attendance of cinemas in specific seasons of the year or a temporarily increased interest of the audience in a certain topic.

Given the high production cost of a film in the traditional model, film-makers usually 14 release the film in the domestic market, with its subsequent release in other countries. In most cases, without such a strategy they would not be able to earn enough revenues to cover the costs and show a profit. Big producers often acquire rights to any possible territories where the film could be exploited, or alternatively lay off the risk by sharing territories with a partner. The revenue losses in some territories are evened out by the higher returns in others. (Finney 2014, p. 4-14)

With respect to the film itself and the uncertainty of the audience regarding its quality, the challenge of the film production and subsequently its marketing strategy is to create a “sales momentum” – an initial consumer recognition which helps them to build sales and generate resources, which can be further used to create wider consumer recognition. The challenge here is to choose the right combination of elements of the “creative package”, such as director, writer, lead cast, genre, music etc. A well-known story can also offer an important hook which the marketing strategy can be based on. Therefore, using underlying material previously known to the public can result in a good way of attracting more attention to the film and more potential viewers. (Finney 2014, p. 131-145) Apart from that, building the screenplay on a pre-existing story might decrease the costs of development, as the main plot has already been created and the screenwriters are only supposed to adapt the story for the purposes of the film.

The knowledge of the influencers of a film’s theatrical success can provide useful information to film producers, enabling them to mitigate the business risk of their project by adjusting the final product to the market needs. By making strategic choices in making partnerships, creating business models, booking screens, budgeting, hiring producers, directors and actors and adjusting the movie’s properties to fit the market, a studio can position a movie to improve its chances of success.

3 Specifics of the market of the European Union

The film industry of the European Union encompasses over 750 000 companies, employing more than 370 000 people and reaping around €60 billion in revenue. When it comes to the EU production, the 'Big Five' – France, Germany, United Kingdom, Italy and Spain – account for around 80% of releases, industry turnover, and persons employed. (European Parliament 2014, p. 3)

3.1 Major operators

The European film landscape is characterised by a strong presence of the Hollywood players. 4 They are vertically integrated firms with activities ranging from production to distribution. Given their financial power and control over the distribution channels, they have raised significant barriers to entry for other smaller companies. (European Commission 2012, p. 27-28) Despite the fact that the share of US-based productions in the EU market reached only 16 % in the period 2005-2014 in terms of the number of films released, their market share in terms of admissions during the same period exceeded 70 %. For comparison, for EU- based productions it was 64 % in terms of the number of films released and 27.4 % in terms of admissions. (European Audiovisual Observatory 2016, p. 18-19) Even more strikingly, the share of the EU film productions5 on the US market in terms of box-office revenues was just 2.5% in 2010. (Wutz and Pérez 2014 p. 45)

Alongside the Hollywood subsidiaries, there are a number of big EU-based studios that are also active at various levels of the film value chain, e.g. Pathé (France), Constantin Film (Germany) or Kinepolis (Belgium). However, the majority of EU-based productions consist of nationally organised companies, many of which are relatively small and focused on one segment of the value network. (European Audiovisual Observatory 2016, p. 31)

3.2 Budgets and profits in the EU film industry

Although EU productions have a significantly weaker position in terms of revenues and market share, this does not necessarily show their worse financial position. Success in

4 Usually referred to as the “big six” - Paramount Pictures, Sony Pictures, Twentieth Century Fox, Universal, Walt Disney and Warner Bros. 5 Without the UK 16 general cannot be measured by market share and box office revenues alone. Consider for instance films that target niche audiences: such films may succeed in reaching their targeted audience and despite low revenues, they can perform in terms of profitability provided that their budgets are sufficiently low.

The average EU production budget is relatively low: it ranges from about €11 million in the UK, €5 million in Germany and France to €300 000 in Hungary and Estonia. For this reason, some European films remain profitable even with low theatrical admissions. (European Commission 2014, p. 3) However, as it is the case of the industry in general, most European films do not recoup their costs.6

3.3 Ambivalence of the EU market – single market vs territoriality

Despite the EU single market and the relatively easy movement of goods and services within its territory, the market is still characterized by strong territoriality constraints. This is mainly due to the factual language fragmentation and the persisting territoriality of copyright regimes. Numbers show that the circulation of EU-produced films is very limited and significantly worse than circulation of their US counterparts. Between 2005 and 2014, a total of 63% of EU films were released in only one country, generally their national market. In addition, 79% of them were released in maximum two countries. On average, EU films were released in cinemas in 2.6 countries whereas US films were theatrically released in 9.7 countries. (European Audiovisual Observatory 2016, p. 17-20)

When it comes to the exporting patterns of EU-made movies, figures also show a relatively small distribution power of European studios: during the years 2011-2015, on average 52 % of studios exported their films within Europe, accounting for 22 % of total admissions. Only 10 % of all produced movies were exported outside Europe, 7 which accounted for 21 % of total admissions. (European Audiovisual Observatory 2015, p. 15) These figures show that a large proportion of non-national admissions are generated outside Europe and suggest that a better distribution strategy focused on non-EU markets could possibly enhance the revenues/profitability of European studios.

6 See for example Bomsel and Chamaret (2008, p. 33): The study reveals that of 162 films produced in France in 2005, only 15 recouped their production and distribution spending on the primary market. 7 Outside Europe here means one of the 12 non-EU markets included in the study. 17

3.4 Challenges of the EU film industry

As the description of the current situation in the market already suggests, the main challenges of the EU film industry are its fragmentation and national focus, problems in terms of seeking for financial resources and focus on production without enough attention paid to distribution and promotion. (European Parliament 2014, p. 4) Another weakness often mentioned with respect to success of film projects in the EU market is the insufficient match between demand and supply. (European Commission 2012, p. 140) The inability of some movies to reach their audiences results in the fact that many films do not manage to break- even.

4 Literature review

There is a rich repository of past studies aiming at identifying film characteristics and other film-related factors which have the power to influence a film's performance in the movie theatres. However, given the factual monopoly of American film studios, vast majority of the studies published to date has focused on the market of the USA. There are several studies analysing the European market to be found, 8 their focus being, however, limited to the territory of one country.

The economic rationale identifying the success determinants is quite straight forward. In most cases, researchers try to identify the main factors that influence the financial performance of a movie, to give the producers guidance on how to increase the chances of their project to break-even. For that reason, it appears helpful to divide the possible influencers into two main categories:

a) Pre-release (or ex ante) factors typically include the budget of a film, its rating,9 genre, timing of release, number of screens on which it is shown or presence of famous actors or other crew members. (Hyatt and Johnson 2016, p. 4-7) When forecasting success, Hollywood studio executives and distributors centre their focus on these factors, because these are the most controllable variables at hand. (Hyatt and Johnson 2016, p. 4)

b) Post-release (or ex post) factors often centre on the perception of the film by the professional as well as lay audience. It thus includes critics’ reviews, awards and award nominations or word-of-mouth reviews. (Hyatt and Johnson 2016, p. 4-7) These factors are largely independent of the sphere of influence of the producers. Despite that, as the literature suggests, they are not entirely incontrollable since the producer/distributor might still have the chance to improve his project’s performance by intensifying the post-release advertising.

8 For example Bagella and Becchetti (1999), Boccardelli et al. (2008), Elliot and Simmons (2008), Gmerek (2015) 9 Rating in this case refers to the label which determines the audience which the film is suitable for. It may be determined by aspects of violence, vulgar language, nudity etc. In the USA, for instance, the rating is determined by the Motion Picture Association of America (MPAA). In the Czech Republic it is set by the film’s distributor and the labels may be MP, MP-12, MP-15 and MP-18, according to the recommended minimum age of the audience. 19

4.1 Measures of financial success

There are different possible approaches to how to measure the success of a movie. When focusing primarily on measures that can be used to assess the financial success of a movie, the following indicators may are usually considered as a dependent variable:

 Box office results (domestic, international or total) represent the amount of money paid by the movie theatres' customers. This indicator is most commonly used when determining the market success of a film.  Theatrical attendance (admissions), although not a financial indicator, can be a suitable measure as well, given that the price in the cinema is usually the same for blockbusters as well as low-budget independent films.  Theatrical rentals (accruing to distributors or exhibitors) provide less accurate estimates of theatrical performance given that they primarily reflect their negotiating skills in capturing the rent generated from ticket sales.  Return on investment (RoI), usually expressed as overall theatrical profits or return on production costs, is less commonly used among authors, some of whom explicitly admit lack of complete data on all components of profitability, such as “prints and ads” marketing and advertising expenditure.

Only very few studies have used revenues from video/DVD rentals and sales or TV broadcasts as a dependent variable. The reason might be the difficulty to obtain such data or the common sense that the film producers' investment is primarily recouped from box office revenues, while other financial sources are only ancillary. (Hadida 2009, p. 301-304)

4.2 Methodology

4.2.1 Linear regression

By far the most commonly used technique to predict success of a motion picture in the current literature is the multiple regression analysis.

Regression analysis is a statistical technique used to fit the observed data into a pre- defined model specified by an estimating equation, in this case linear. It generates coefficients for each independent explanatory variable in the equation. The coefficients represent the change in the dependent variable resulting from a unit change in the corresponding

20 independent explanatory variable ceteris paribus. (Litman 1983, p. 167)

The first one to develop a multiple regression model to identify determinants of motion picture theatrical success was Barry Litman, who examined a sample of 125 films released in the USA in the period 1972 to 1978. The dependent variable chosen by Litman was domestic theatrical rentals accruing to the distributor. (Litman 1983)

The work of Litman was followed by many, applying the technique of linear regression to different data sets and testing other possible influencers including them into the multiple regression models. Some authors 10 have used the stepwise procedure commonly available in statistical software, where the choice of explanatory variables is made automatically, based on their computed significance in the final model.

4.2.2 Path analysis

Path analysis is an extension of linear regression, which some authors have considered a more suitable approach as it offers the possibility to not only test the direct relationships between potential success drivers and financial success of the movie, but also to examine the interrelationships among the drivers. In the beginning of the analysis, it is up to the researcher to create the input path diagram, which represents the causal connections between the variables that are included in his hypothesis. The results are represented in the output path diagram. (Webley and Lea 1997) Path analysis considers the model as an interdependent system of equations and estimates all structural coefficients at the same time. By doing that, it allows for the separation of direct and indirect causal effects in cross-sectional data sets. (Thurau et al. 2006, p. 16)

Pointing out the shortcomings of the standard multiple regression, Thurau et al. (2006) were the first to use the path analysis to analyse the determinants of theatrical success of a film. They identified a set of hypotheses concerning interrelations between dependent variables included in two models with a differing dependent variable – long term domestic box office and profitability.11 They tested their hypotheses against the sample of 331 films released in the USA in the years 1999-2001.

The limitation of the path analysis is that it is most likely to be useful when one

10 e.g. Iacobucci et al. 2010, Pangaker and Smit 2013 11 Profitability was defined as the difference between total North American box office and the film’s production and advertising costs. 21 already has a clear hypothesis to test, or a small number of hypotheses, all of which can be represented within a single path diagram. It is little used at the exploratory stage of research. In addition, the analysis is not able to establish the direction of causality. (Webley and Lea 1997)

4.2.3 Three-stage least squares

Elberse and Eliashberg (2003) examined a sample of 164 films released in the USA in 1999 and their performance in the domestic, as well as selected European markets. In consistence with their hypothesis that determinants of box office revenues change once the opening weekend box office data becomes available, they make a conceptual distinction between the first week and the subsequent weeks from the release. They employed the three- stage least squares (3SLS) procedure to estimate a system of equations. OLS was regarded as inconsistent because the endogenous variable screens used as a regressor in the revenues equation was contemporaneously correlated with the disturbance term in the same equation; the presence of lagged endogenous variables also made it biased. Furthermore, as the errors across equations may have been correlated, a 3SLS procedure was considered more efficient than a two-stage least-squares (2SLS) procedure.

The 3SLS procedure was also used by Liu et al. (2014) for testing a set of hypotheses regarding the influence of star power.

4.2.4 Alternative techniques – modelling the probability mass

De Vany and Walls (1999) discovered that box-office revenues are asymptotically Pareto-distributed and have infinite variance, which undermines the assumptions of the classical linear regression model. Therefore, forecasting expected revenue is imprecise and lacking in foundation. However, if the impact of certain variables on the probabilities of certain outcomes can be predicted, then better choices might be possible. (De Vany and Walls 1999, p. 306) They modelled the distribution of probability mass of movie outcomes12 and examined the probabilities of extreme outcomes, which they defined as a box-office revenue exceeding $50 million. Using a sample of 2 015 films released in the US between 1984-1996, they examined the probability that a movie will become a hit. They carried out this exercise by modelling the conditional hit probability as a function of the film's budget, star presence,

12 Where they considered profit as the outcome, they defined it as 0,5 x revenue – budget.

22 genre, rating, year of release, survival time and number of opening screens.

4.2.5 Alternative techniques – artificial neural networks

Questioning the appropriateness of the commonly used linear modelling, Sharda and Delen (2006) were one of the first ones to use the artificial neural networks, a biologically inspired analytical technique capable of modelling complex non-linear functions.

The authors divided 834 films in their data set (released in the USA between 1998 and 2002) into 9 categories according to their domestic box-office performance and used the neural networks analysis to identify the factors that influence which category the film falls in. The technique employed in their study was able to predict the success category of a movie with 36,9% accuracy, which is reported by the authors as a significantly better accuracy than when other techniques are used. Ghiassi et al. (2015) built upon work of Sharda and Delen and used the dynamic artificial neural networks (DAN2) to predict film success category using only the pre-release factors.

However, it cannot be generally concluded that artificial neural networks outweigh logistic regression without testing appropriateness of each method on a specific case. (see Sayeh and Bellier 2014)

4.3 Factors of success

The following determinants were subject to examination in the past and were identified, with more or less differing results, as having an influence on the films’ financial success:

4.3.1 Pre-release factors

Production budget has been identified as one of the major determinants with positive influence on revenues.13 However, the results are the opposite when RoI is used as a proxy – high budget films appear to be less profitable than others, indicating that studios generally overspend. 14

According to Lampel and Shamsie (2000), production budget plays the role of a

13 Litman (1983), Ravid (1997), De Vany and Walls (1999), Thurau (2006), Karniouchina et al. (2010), Hasbrouck and Deniz (2012), Gmerek (2015) 14 Ravid (1997), Thurau et al. (2006), Karniouchina et al. (2010), Bozdogan (2016) 23

“signalling property” to the audience – the higher the budget, the more elaborate plot, more special effects, more star actors etc. Moreover, their model included also all possible combinations of interactions between the production budget, distribution strategy and critical response, finding that there is a strong contribution that is made by the combination of these three factors. Thurau et al. (2006) confirm the positive influence of budget on theatrical revenues. However, according to their findings, it directly influences only the opening weekend box-office. The long-term box office is influenced by it only indirectly – through the short-term effect.

Certain genre types have been identified as significant determinants by some researchers. According to Litman (1983), the science-fiction genre is positively associated with film’s financial success. Topf (2010) finds comedy and action, Terry et al. (2010) action and children and Hasbrouck and Deniz (2012) animation to be significant in terms of theatrical revenues. In Italy, it is the comic genre what matters (Bagella and Becchetti 1999), 15 in France, comedy or romantic comedy (Bozdogan 2016) and in Poland, documentary or comedy (Gmerek 2015). Sharda and Delen (2006) and Karniouchina et al. (2010) find horror movies to be associated with lower revenues; however, because of their significantly lower budgets, horrors are positively associated with RoI. Hasbrouck and Deniz (2012) explain it by the fact that horror movies usually do not have stars in them, which increase costs significantly. On the contrary, western and sci-fi movies generate significantly lower RoI due to their inflated budgets. (Sharda and Delen, 2006)

The results regarding the impact of genres are generally not very consistent. For instance, Karniouchina et al. (2010) found film genres to matter more than previously suggested. In contrast, Pangaker and Smith (2013) only found drama to be negatively associated with global box office revenue, concluding that in the contemporary global film market the genre does not play such an important role anymore.

Pankager and Smith (2013) found a positive relationship between family-friendly MPAA rating, a concept used in the USA to categorize films according to their suitability for certain groups of audiences and total box-office revenues. However, most studies found rating to be largely irrelevant as predictors of theatrical success of movies, implying that it is the content and overall quality of the film that interests the audience rather than the restrictions

15 after controlling director and actor effects 24 placed on who can attend.16

A certain level of the cultural familiarity of the concept may be a strong signalling property indicating the product’s quality. This reasoning is based on the theory of “brand extensions”, a theory suggesting that well-established brands are likely to attract customers in other markets as well thanks to their good reputation.17 Conceptualization of sequels as brand extensions thus suggests that movie goers who liked the original would be more likely to see the sequel, thus providing an increase in first-week and total attendance. The same reasoning goes for prequels and adaptations. Peltoniemi (2015, p. 45) adds that the nature of films as experience goods results in the desire of consumers for a certain level of familiarity – to better understand the product – and a certain level of novelty – to enjoy it. Thus, theory would suggest that sequels and adaptations should outperform purely original films, provided that they keep on being innovative.

This theory has been generally confirmed by many authors. 18 Karniouchina et al. (2010) adds that sequels are associated with advantages in terms of both RoI and revenue, but contrary to common speculation, they cost less to produce than similar original films. Hasbrouck and Deniz (2012) have found a positive relationship between domestic theatrical revenues and the film being a sequel. However, the film’s characteristic as a remake influences profitability negatively.

Some authors focused not only on whether the film was a sequel or not, but rather whether it was a sequel to a successful movie. Orlov and Ozhegov (2015) provided support for the hypothesis that sequels are successful only due to the fame of the first part of the series, otherwise they do not excel one-part movies in terms of the box office. Similarly, Thurau (2006) finds a strong influence of the success of the film’s predecessor on both long- and short-term box office, as well as on profitability. Finally, it seems that sequel movies tend to reap more revenues although they receive lower user ratings than original films, which indicates a strong brand extension effect. (Ravid 1997, Iacobucci et al. 2010)

The topic of adaptations has been subject to much less research, due to the

16 Litman (1983), Ravid (1997), Sharda and Delen (2006), Karniouchina (2010) 17 See for example Aaker and Keller (1990, p. 38): „Subejcts‘ perceptions of the quality of the original brand, quality, and the relationship or „fit“ between the original and extension product classes had an interactive effect on evaluation of an extension.“ 18 Prag and Cassavant (1994), De Vany and Walls (1999), Terry et al. (2010), Topf (2010), Pangaker and Smith (2013), Gmerek (2015) 25 complexity of determining whether a film falls within this category. Films employing adapted content often do not share the same title with the original piece of work and in many cases the decision on whether the film would qualify as an adaptation is rather a question for a judge than a researcher testing his hypotheses on large data sets. Despite these obstacles, there are some studies which confirm the advantage of adaptations in comparison to wholly original films. This advantage can be not only of a financial nature, but it appears that, in some cases, it may also be a good strategy to speed up the project development at an early stage.19

Joshi and Mao (2012) examined the role of adapted content as a signalling property, comparing performance of adaptations during the opening weekend and later. The tested hypothesis was based on the brand extension literature, which suggests that consumers are less likely to use the original brand to judge an extension when extension quality information becomes available. Their findings confirmed that book-based movies perform better at the box office on the opening weekend than original movies. However, this superior performance dissipates after the opening weekend. Furthermore, the opening weekend performance of book-based movies is positively driven by book equity and book-movie similarity, and negatively by the time lag between the book’s peak equity and movie release. After the opening weekend, many of these book-related variables cease to have an impact, and the effect of other movie-related signalling properties increases.

Apart from the content itself, Kim (2013) finds that also the length of the film has an impact on theatrical success – the longer the movie is, the more audience it attracts.

One would expect major studios/distributors 20 to have better access to the preferential theatres and more extensive distribution connections. This reasoning is in line with findings of Litman (1983), Bagella and Becchetti (1999) or Pangaker and Smith (2013), who have all found significant positive effect of release by one of the majors on the film’s theatrical success. In addition, Chang and Ki (2005) found that the length of the film run mostly depends on the distributor: films released by major distributors were screened in theatres longer.

Interestingly, the analysis of Karniouchina et al. (2010) shows that major producers

19 See for example Luo (2014), who finds out that adapted content is more likely to be greenlighted for film production. 20 Vertical integration is a common phenomenon in the world motion picture industry. Therefore, in this thesis both of these terms can point at the entity which releases the film. 26 and distributors are associated with lower revenue and profits once budgets and screens are controlled for, meaning that although they tend to utilize higher budgets, they do not produce more successful movies with them.

The literature focusing on the impact of major studios is rather scarce. The same goes for co-productions. However, the study of Bozdogan (2016) is worth mentioning – according to her findings, co-productions perform worse in terms of theatrical revenues in French cinemas.

Presence of famous stars and directors has been frequently examined. To measure their impact on film success, authors often use the variable star power or director power, which can be defined in various ways – most often either as the box-office of their previous films,21 their popularity22 or as a binary variable indicating whether a famous actor/director was involved.23

The original study of Litman (1983) did not confirm any relationship between the presence of stars and film success. Out of his findings, Litman draws a conclusion that the superstars’ presence is relevant only to the extent that they contribute fine performances and enhance the quality of the movie. Thurau et al. (2006) even found slightly negative indirect effect of star power on both long and short term box office. However, recent studies indicate opposite results.

According to Kim (2013), the main actor or actress in a movie will most likely determine whether or not this movie is successful in generating revenue. This also happens to be the same when hiring a director. De Vany and Walls (1999) have found that superstar presence shifts the probability mass to higher outcomes. Karniouchina (2010) examined the impact of movie buzz and star buzz on opening weekend and long-run box office revenues, defining the variables as “the overall excitement and anticipation related to a title or a top star who is involved in the project, obtained from search-based measures”. The results indicate

21 Kim (2013) – the average gross of the actor’s movie Liu et al. (2014) – the average box office revenue of a star’s most recent five movies (commercial success) Thurau et al. (2006) - average box-office of the director’s three most recent films 22 Liu et al. (2014) – the total number of Oscar nominations and awards that the star had received before the film’s release (artistic success) Thurau et al. (2006) - sum of Hollywood Reporter’s Star power ratings for all stars listed on the movie poster 23 Litman (1983) - top 10 box office stars for the previous two years before the film's release date Ravid (1997) – presence of a director or actor who appeared in top 10 box-office movies of the previous year, presence of a director or actor who had won an Oscar 27 that movie buzz is instrumental in boosting box office revenue throughout the theatrical release. Star buzz enhances opening week box office receipts; however, it can also have a negative impact on revenue during subsequent weeks if the underlying movie fails to resonate with audiences.

Ravid (1997) tested the “rent capture hypothesis”, assuming that stars capture their marginal value, and therefore do not contribute to the RoI of the film. Their findings largely support the hypothesis: films which employ stars are generally more expensive, have higher revenues, but lower RoI. When Ravid (1997) introduced budget into the revenues model, the variable budget took all the significance – high budget signals high revenues, regardless of the source of spending. Similar conclusion was drawn by Liu et al. (2014), whose findings confirm that stars tend to attract financing. Including a star in the film usually implies a higher budget; therefore, its main impact is through the higher budget.

De Vany and Walls (2002) have reported that motion picture profit has a stable Pareto distribution with finite mean and infinite variance. Its skewed shape accounts for what they call the “curse of the superstar”, a parallel to Ravid’s rent capture hypothesis, stemming from the difference between the average, expected and the most probable profit for superstar movies. The skewness of the distribution causes the expected value to be substantially larger than the most likely outcome, hence if a studio pays the superstar its expected contribution to profit, the movie will, with high probability, lose money.

In the European cinemas, star power has been frequently identified as an important influencer of films’ theatrical success.24 Contrarily to the results for the American market, Boccardelli et al. (2008) find that distinctiveness of actors, measured as the stock of awards and award nominations previously gained by the actor, negatively influences box-office revenues. The director power appears to have a strong impact on the attendance of the European cinemas (Bagella and Becchetti 1999) confirming the leading role of movie directors in the European film market. (Gmerek 2015, p. 19)

The influence of star presence in films was not tested in this thesis. The reason for it is the high number of actors involved in the sample and the difficulties it would assume to include the star factor in the model, without comprehensive data on revenues of their previous films.

24 Boccardelli et al. (2008), Bagella and Becchetti (1999), Gmerek (2015), Bozdogan (2016) 28

Release date is an important strategic decision, which requires the producer to consider two basic phenomena, whose combination may affect theatrical revenues – the cinema attendance and competition. Authors usually focused on before-Christmas and summer release, basing their hypotheses on the rationale that in certain months movie-goers attend cinema more frequently. This increased demand gives incentives to producers to release their films around these dates, which in turn increases competition. The final influence of the release date on the financial success of a movie depends on the evolution of the balance between these two factors during the year.

Litman (1983) identified Christmas release as positive influencer of theatrical success, but found no relationship between summer release and the latter. Thurau et al. (2006) discovered a positive influence of summer release on opening box-office results, while the positive effect on long-term box office is diminished by the fact that a summer release reduces movie's chances to become awarded. Films released in Poland in April, July and August performed significantly worse than others. (Gmerek 2015). Pangaker and Smith (2013) found no significant impact of film’s release date around holidays.

Gmerek (2015) focused specifically on the intensity of competition and its influence on movie’s performance in Poland. The results indicate that simultaneous release of a competing Polish or foreign movie significantly decreases domestic box-office revenues of a film. Sharda and Delen (2006) found no relationship between increased competition and motion picture’s financial performance.

Some authors also focused on the number of screens, which is quite an intuitive factor positively influencing theatrical success. The more widely accessible a film is, the more audience it is likely to attract. 25 Elberse and Eliashberg (2003) discovered that several variables usually assumed to influence revenues directly, have only indirect impact on them throughout the allocation of screens. Advertising expenditures emerge as a particularly good example in this respect. Furthermore, the number of screens and (expected) revenues appear to be highly interrelated. The number of screens is the key determinant of revenues and expected revenues in turn the key determinant of the number of screens.

25 Lampel and Shamsie (2000), Sharda and Delen (2006), Gmerek (2015) 29

4.3.2 Post-release factors

The signalling properties, which emerge after the film’s release, are in principle of two kinds: critical acclaim, expressed by critics’ reviews and awards, and consumer reviews.26

Critics’ reviews have been largely identified as positive influencers of movies’ theatrical performance. 27 Furthermore, Elliot and Simmons (2008) suggest that positive critical reviews appear to attract more advertising.

In the United States and the UK, critical acclaim plays a surprising role – it is positively related to opening week revenues but negatively related to opening week screens. The latter may reflect distributors’ power to negotiate a wider opening for critically unacclaimed films. (Elberse and Eliashberg 2003, p. 350) Gmerek (2015) even found a negative relationship between positive critics’ reviews and Polish domestic box-office revenues.

Academy Awards (Oscars) or Oscar nominations were also frequently found to boost revenues28 and profitability. (Thurau et al. 2006) Sparrow and Simonoff (1999) add that this effect is only present in case the movie has not already been in release for many months when the nominations are announced. Academy Awards and critics’ reviews are in fact the same category, since the decision on who to award an Oscar is taken by a panel of critics. This is confirmed by the findings of Thurau et al. (2006), according to which critics’ reviews strongly correlate with awards and consumers' quality perception.

Consumer’s perception can be in principle measured in two ways: either quantitatively – as the volume of reviews, or qualitatively – as their valence, measured typically by the mean rating of the movie (Chintagunta et al. 2010) or the proportion of positive and negative messages. (Liu 2011) While some authors simply find positive effect of consumers’ perception on revenues29 or profitability,30 some researchers have gone deeper, trying to gain better understanding of whether it is the volume or valence of user ratings that matters:

26 Also referred to by some authors as the “word-of-mouth“, or recently rather the “word-of-mouse“. 27 Litman (1983), Ravid (1997), Lampel and Shamsie (2000), Elliiot and Simmons (2008), Topf (2010), Bozdogan (2016) 28Litman (1983), Thurau et al. (2006), Terry et al. (2010), Pangaker and Smith (2013) 29 Ravid (1997), Gmerek (2015), Bozdogan (2016) 30 Thurau et al. (2006), Karniouchina et al. (2010) Ravid (1997), interestingly, finds a negative relationship between positive user reviews and profitability. 30

Lee et al. (2015) worked with the hypothesis that online product reviews created by users based on individual usage experience can serve as a new element in the marketing communications mix and work as free “sales assistants” to help consumers identify the products that best match their preference. As consumers become more engaged and generate opinions about products in the online environment, consumer-created information becomes prominent and more relevant to consumers than other product information created by sellers. Therefore, as they hypothesize, it may be a profitable strategy for a firm to post as a customer online or to alter strategically online consumer reviews. They found that both volume and valence of online user ratings are important when predicting box-office sales. Furthermore, they suggest that improving the volume and valence of ratings can have equivalent effect as ad spending can provide.

Liu (2011) has found that word of mouth information offers significant explanatory power for both aggregate and weekly box office revenue, especially in the early weeks after opening. However, most of this explanatory power comes from the volume of word-of-mouth, not its valence as measured by the percentages of positive and negative messages. Duan et al. (2008) also confirm this hypothesis – higher ratings do not lead to higher sales, but the number of posts is significantly correlated with movie sales. Businesses shall therefore focus more on the mechanisms that facilitate consumer word-to-mouth exchange rather than try to influence online ratings. However, the authors add that this finding can only disclose the functioning of the underlying procedure of word-of-mouth, while the sites themselves can have no significant impact on consumer decisions to purchase. In contrast with previous studies, Chintagunta (2010) has found that it is the valence of user reviews that seems to matter and not the volume.

Finally, Escoffier and McKelvey (2015) have gone deeper in examining the “wisdom of crowd” effect in predicting movies’ box-office performance. According to their findings, the wisdom-of-crowds effect resulting from the independent evaluations of a small crowd is more accurate than the evaluations of a small number of experts, especially when the question calls for subjective answers. Although the ability of the audience to predict is lower under the conditions of social influence, it increases again over time. Based on these results, the authors suggest that film producers use the crowd wisdom offline at the development stage to get a true measurement of movie’s quality, and crowd wisdom online at the marketing stage to develop a positive social media influence.

Some authors examined also advertising for its supposed key role in transmitting quality signals to potential audiences. That is, advertising may well be largely a mechanism to stimulate audience awareness of the quality signals that are present in films. (Elliot and Simmons 2008, p. 20) However, one limitation of their studies is the lack of reliable data, as the studios usually do not make their advertising expenditures publicly available. Therefore, it is a difficult exercise to obtain a testable data set which would ensure trustworthy results.

Thurau et al. (2006) find that advertising influences all profitability, short-term as well as long-term box-office and is interestingly interrelated with other dependent variables. It has a remarkably strong impact on consumers' quality assessment, which is a finding which helps increase understanding of how advertising influences movie success. According to Elberse and Eliashberg (2003), advertising support is a key predictor of opening week revenues and screens (i.e., a movie’s marketability), while in subsequent weeks its function is taken over by the word-of-mouth communication (influencing a movie’s playability). Iacobucci et al. (2010) also find that high advertising spending on movies supported by high ratings maximizes the movie’s total revenues.

The authors frequently overcome the obstacle created by the lack of data by simply stating that advertising expenditure is highly correlated with the production budget.31 The influence of advertising can thus be measured through inclusion of the budget variable into the model. Liu et al. (2014, p. 8) stated the following: “In industry practice, advertising spending is often a fixed percentage of the production budget. Moreover, from the econometric perspective, production budget and advertising spending are highly correlated: including both might cause multicollinearity concerns.” Hence, it is the production budget that is the driving force. Accordingly, after introducing the advertising expenditures into their model, the results did not change significantly.

Finally, in accordance with the saying “success breeds success”, opening weekend box-office was found by many authors to have a particularly strong impact on long-term box office.32 In addition, Terry et al.(2010) examined the performance of a sample of English- language films produced for primary release in the USA in selected foreign markets. Their

31 See for example Prag and Cassavant (1994), who found that marketing expenditures are positively related to production costs, Thurau et al. (2006), reporting significant impact of production costs on advertising expenses, Ghiassi et al. (2015), observing no change in explanatory variables after including pre-release advertising in their model, claiming that films with greater budget are advertised more. 32 Sparrow and Simonoff (1999), Thurau et al. (2006), Gmerek (2015) 32 results confirmed the hypothesis that the domestic performance of a film serves as an important predictor of the foreign box-office revenues. The extremely strong relationship between the domestic box office and foreign box office might simply imply that domestic performance is a quality signal to the foreign market. On the other hand, the domestic and foreign market correlation might also be explained by the cultural blending across countries creating common interest and preferences. (Terry et al. 2010, p. 10) Elberse and Eliashberg (2003), found strong support for this hypothesis too; however, the effect in their model is somewhat moderated by the time lag between the releases.

Finally, De Vany and Walls (1999) have identified the long run to be the most important factor associated with a movie becoming a hit, which they consider a clear evidence that the audience decides a movie's fate at the box office and no amount of star power, screen counts, or promotional hype is as important as the public's acceptance of the film. However, it can be objected that the long run has no explanatory power, and should be used as a dependent rather than an independent variable in the model.

5 Hypotheses

The data used for this thesis allows testing several hypotheses related to the efficiency of certain risk-mitigation strategies on a novel sample containing only European films and co- productions involving European studios. The general idea behind the analysis is that film producers are able, to a certain extent, to influence the theatrical success of their film by choosing attributes that are more likely to succeed with audiences or undertake certain steps to enhance the likelihood that the film will reach its audiences. The main focus is thus on the pre-release factors. Testing of the following hypotheses is supposed to shed more light on the drivers of theatrical success of European films.

First of all, the influence of film attributes will be tested. As suggested by previous research, the adaptation strategy might be advantageous, since it offers a combination of a certain level of familiarity of the concept and elements of novelty. Therefore, films characterized as adaptations are assumed to attract more consumers to the cinemas. Applying the same logic, film genres might also serve as a signalling property of a movie and give a hint to the audience regarding what to expect from it. It is assumed that certain genres are more popular than others, these attributes are thus also supposed to influence the movie’s performance.

Attracting a partner from a different country can be another possible strategy. In this way, the production studio can raise funding not only because there are more entities involved, but also because it can enjoy national public support schemes in multiple states. Furthermore, involving more entities can lead to a synergic effect coming from the different experience the co-producers have and skills they are endowed with, and may facilitate the entry of the film into more national markets.

The analyses focused on the market of the United States have generally rejected hypotheses related to the influence of a film director on the movie success. The scarce research on the European market, however, suggests that the European audience perceives a popular director as another indicator of the film’s quality. The sample of films produced in the EU thus offers a good opportunity to test whether a popular director has the power to attract more people to the cinemas in the European market conditions.

The cinema attendance and level of competition fluctuates across the year. The film’s release is the last stage which gives the producer the opportunity to take decisions in order to 34 enhance the chances of success. The seasons which appear to be sticking out in terms of theatrical attendance are summer and Christmas. In the first mentioned case, the rationale lays rather in the assumption of decreased competition – summer months are usually not a season of release of blockbuster movies produced by big studios. In the latter case, the assumption is that although many producers decide to release their film before Christmas, the enhanced attendance of cinemas in this period compensates for the increased competition.

The set of hypotheses can be summarized as follows:

H1 → Films using adapted content perform better than original films.

H2 → Films of certain genres perform better than others.

H3 → International co-productions have higher budgets and perform significantly better than national films.

H4 → Films employing a well-known director perform better.

H5 → Films released in the summer months or before Christmas perform better than films released in other seasons.

6 Data source: The Internet Movie Database

The data set contains budget and revenues estimations and a wide range of film characteristics, available to the producers prior to, as well as after the theatrical release of the film. The original data comes from the Internet Movie Database (IMDb.com).

According to the information provided on its website, IMDb is the world’s most popular and authoritative source for film information. It offers a searchable database of more than 185 million data items including more than 4 million films and TV shows, including big screen and direct-to-DVD features; web series; documentaries; video games; music videos; experimental films; short films and commercials. (IMDb 2017, About IMDb)

To be included in the database, a work has to be of general interest to the public and should be available to the latter. General interest of the public is assumed when the film was released in cinemas, shown on TV, released on video or on the web, listed in the catalogues of established video retailers, accepted or shown at film festivals, made by a famous artist or person of public interest. The same is assumed if the work is famous, widely talked about and referenced in the media or the “film community” or is of historic interest. (IMDb 2017, Title Eligibility)

The database is created by its users, while each entry is checked by the IMDb data editors to ensure its reliability. To be accepted, the new submission must include an easy-to- verify proof of eligibility. For a released film this may be a link to online evidence of theatrical release, TV showings or screenings at qualifying festivals. (IMDb 2017, New Title Submission) IMDb retains the right to reject any work whose eligibility according to the above rules is dubious or unverifiable.

6.1 Qualitative data on films

The database contains an extensive list of film information, starting with the basic film characteristics such as country and the year of production, language, runtime, production studio and distributors, cast and crew, genre and storyline or release countries and dates. Furthermore, the website contains user reviews, rating and the list of awards. Apart from the information linked to a specific film, it also contains information related to individuals, including actors and directors, and media companies.

IMDb also offers a variety of lists of popularity (of films, actors, directors etc.) based on different criteria. Some of these lists were also used for the analysis.

6.2 Financial data on films

In IMDb, financial data is available only for some films. It usually contains budget estimation and box-office revenues for the opening weekend as well as in total. The completeness of data varies from one film to another and is often missing for low-budget independent films. However, the data contained in IMDb is currently the most reliable source of information on the worldwide film revenues, as it derives the information from Box Office Mojo, an online box-office reporting service that is currently owned and operated by the IMDb. (Box Office Mojo 2017, About Box Office Mojo)

6.3 Additional data sources

The financial data on films (budget and box office revenues) were converted to the common base of US dollars using exchange rates available in OANDA, a Canadian-based company offering exchange rates services. (OANDA 2017, Currency Converter) For a better comparison, the financial data coming from different years was standardized to the base year 2000 using the annual data on consumer price index in the USA published by the Bureau of Labor Statistics of the United States of America. (US Bureau of Labor Statistics 2017, Consumer Price Index Data from 1913 to 2017)

7 Description of the data

The data set used for the analysis consists of 1457 films produced and released in the territory of the European Union between 2000 and 2010.33 The years 2000-2010 were chosen with the aim of analysing a recent sample, but to ensure that the key data will be as complete as possible. The criteria for inclusion in the data set were the following:

- the entry is not a documentary, TV show, broadcasting, video game, prequel or sequel - at least one of the producers has his seat in the EU territory - the film was released in at least one EU country - the data on theatrical revenues (box office) of the film is available - the data on production budget of the film is available34

7.1 Financial data on films

7.1.1 Box office

The term “box-office” on the IMDb website refers to the worldwide theatrical box office revenues. Other possible sources of revenue such as TV licenses, DVD sales and rentals, product placement fees etc. are not included in the IMDb/box office tracking. (Box Office Mojo 2017, Box Office Tracking by Time) For the purpose of this thesis, the most recent worldwide revenue estimation was taken into account. If this data was not available for the film, it was replaced by the sum of the most recent revenue estimations from each available country. Since the currency in IMDb is not standardized, the data were converted to a common base of US dollars using the average exchange rate for the year of production of the film, calculated on the basis of historical daily quotes available from OANDA. The rationale of choosing the worldwide revenues as an indicator of the movies’ financial success is straight forward – high theatrical revenues are naturally a goal of every movie producer. To achieve that, they often release their film in many countries to gain an additional source of revenues.

33 Croatia has been excluded from the data set, since in 2010 it was not a member of the European Union yet. On the other hand, with respect to the European Single Market, also films from Iceland and Norway and Switzerland were included. Therefore, where the text refers to the European Union or Europe, it refers to the EU plus the three mentioned countries. 34 The data on the production year, genres, Academy Awards and rating also had to be available. This is, however, the case for most of the movies; therefore no films were excluded from the sample based on lack of this kind of data. 38

Exploring only one country or a certain territory might not give a clear picture of the real financial standing of a film. Moreover, the worldwide data from IMDb appears to be more reliable, as for some films the break-down into different countries is not available.

7.1.2 Profitability

Apart from revenues, the revenue/budget ratio has been chosen as an approximation of profitability for the following reason: For some high-budget films it may be the case that although the total revenues are extremely high, the film may not manage to recoup its production costs. This is in consistence with the high-risk profile of the industry itself, as described above. On the contrary, there is a great number of films which, despite their pitifully low theatrical revenues, can reach the break-even point very soon thanks to their small budgets. Such a movie, although it may seem to be a failure in comparison to blockbusters earning millions of dollars, can result in being a much greater business success. Therefore, there is a distinction made between these two indicators.

7.1.3 Production budget

Production budget is expected to be the major influencer of revenues as well as profitability. As in the case of box office data, the budget currency is not standardised in IMDb – most often financial data is set in the currency of one of the producers’ countries. The original financial data has been converted to the common base of US dollars and re-calculated to correspond to its value in 2000.

7.2 Qualitative data on films

With respect to the main focus of the analysis on the pre-release factors, the chosen regressors are mostly those that can be influenced by film producers or distributors prior to the theatrical opening. However, to enhance the explanatory power of the model, some characteristics that become apparent only post-release, have been included as well.

7.2.1 Film genres

The films in IMDb are categorized in 22 main genres (while one film may fall into more categories). Documentaries were excluded from the data already in the beginning and there can be no movie of the genre film-noir, which were produced in the mid-20th century. Thus, 20 binary variables in total have been created to describe the genre of the films.

7.2.2 Use of underlying material

The binary variable for adaptation has been created based on a unique data set created in cooperation with the European Observatory on Infringement of IP Rights. At first, a subset of films has been pre-identified as possible adaptations based on the keywords associated with each film in IMDb35 and the description of the writer’s contribution in the section “credits”.36 Only films based on other works than films were included, thus, the data set does not contain data on sequels.37

Based on the above described criteria, 6937 films have been chosen for a subsequent manual check, which aimed at verifying the information contained in IMDb to the greatest extent possible, using mainly internet sources. In general, based on the case law of European as well as US courts, it was assumed that a work can be with certainty considered an adaptation 38 if the use of the underlying work is so easily identifiable, that it can be recognized by a lay person.39 It must be noted that a certain level of simplification has been applied in this case. A decision whether a work is wholly original or it uses adapted content often requires a sophisticated legal analysis, which was not feasible for the purposes of creating the data set. Certain margin of error thus must be admitted.

Most of the adaptations in the data set are based on books. Some of the films also adapt plays, operas, fairy-tales or legends.

7.2.3 Quality indicators – awards and user rating

Both awards and rating belong to the category of information that becomes obvious

35 Keywords are identifiers used in IMDb to describe basic characteristics of the film. For our data set 87 keywords were identified (such as based-on-novel, based-on-play, adaptation etc.), which indicate that the film may be an adaptation. 36 The role description is not standardized in the IMDb. Thus, there are over 500 role descriptions associated to writers. Out of those, the project team identified 125 roles indicating that the film may be based on a pre-existing work (such as “based on a novel by”, “based on a book by” etc.). 37 The reason for this is that the data-gathering process was focused on films adapting material in the public domain (not protected by copyright). In the next step the information on whether the underlying work is still copyright-protected or already in the public domain was added, which would not be feasible for sequels due to the complexity of copyright in the film industry. 38 in the terminology of the courts of the USA „a derivative work“ 39 See for example in the USA: Knitwaves, Inc. v Lollytogs Ltd., Inc.(1995): “The fact finder decides whether an average lay observer would recognize the alleged copy as having been appropriated from the copyrighted work.” (United States) And in the United Kingdom: Francis Day and Hunter v Bron (1963): The character of the causal connection must be of a sufficient objective similarity between the infringing work and the copyright work, or a substantial part thereof. Ladbroke v William Hlil (1964): The issue of what amounts to a “substantial part” does not depend on a quantitative test, but rather on a qualitative one. 40 only after the film’s release and are largely uninfluenceable by the producers. However, the film quality perception by critics, expressed by awards or award nominations, and by users, expressed by online reviews or rating, was considered valuable information which should not be left aside. The theatrical revenues are highly dependent on the preferences and behaviour of the audience, which is prone to being influenced by the opinions of others. Award ceremonies also help to promote films, which might strengthen its explanatory power in the model.

To assess the user experience, two sets of information available in IMDb could be considered – the user rating and the number of votes. The number of votes could serve as an indicator of quality in the first weeks of the movie’s screening; however, after a longer time it is not possible to decide whether the number of user reviews influenced the theatrical revenues or the other way around. Due to the fact that it was only possible to extract data on number of votes to the present day, this indicator was omitted since its use could lead to an endogeneity problem within the model. On the contrary, user rating is not likely to change considerably over the time, and therefore its use was considered an appropriate indicator of the film’s popularity among audiences. IMDb allows its users to rate films with stars from 1 to 10, with 1 being the lowest and 10 the highest score. The rating value represents the average of all individual user ratings of the respective film.

As for awards, the Academy Award (Oscar) nominations were taken into account. Oscars themselves were omitted since the number of films in the sample which have won the prize is fairly low – 40 for the whole sample and 3 for the subsample of solely European films. However, it is supposed that a nomination can serve as a good quality indicator.

7.2.4 Information on producers – co-productions and major studios

A film is considered a (international) co-production if it is produced by at least two entities, whose seats are in different countries. Given that the data set contains co-productions with US-based studios and in many cases a big Hollywood studio is involved, it was considered necessary to identify involvement of a major studio in the production and include this information in the analysis. It has been established by the previous research that major studios, thanks to their financial power and distribution networks, have better position in the market, and by including this variable in the models it was possible to better interpret the results concerning co-produced movies.

The variable major studio was created as a dummy variable identifying if the film was produced by at least one of the biggest European or international film studios. The gross earnings of the respective company were chosen as the decisive criterion. Given that the data set contains co-productions with US-based studios and the fact that the Hollywood majors habitually have European-based subsidiaries, the “Big Six” and their subsidiaries were given the status of a major studio. The subsidiaries were chosen based on the information provided on the company’s website, if available,40 otherwise based on the information on the company contained in Wikipedia.41

For identification of the European majors the report of the European Commission’s Joint Research Centre was used, which lists 40 highest grossing European film companies (European Commission 2012, p. 30). The total of 11 of these 40 European companies have a film-making subsidiary.

7.2.5 Director

To test the director power, a binary variable has been created, which identifies films that employ a well-known director. The definition could have been based on two different criteria: the sum of box office revenues of the director’s films or the popularity of the director. The latter approach was considered more appropriate (although it supposes certain level of subjectivity), since the use of box office data could lead to an endogeneity problem.

To identify a well-known director, four sources of information were combined: a list of 50 greatest directors according to the panel of experts from Entertainment Weekly (Entertainment Weekly 1996, The 40 Greatest Directors and their 100 Best Movies), 40 best directors according to critics (The Guardian 2003, The World’s 40 Best Directors), top 25 directors of the last 20 years according to IMDb editors (IMDb 2010, The Top Directors of the Last 20 Years) and the first 100 directors by IMDb user rating (IMDb 2012, Top 250 Directors by IMDb Rating). The variable director thus identifies films which employ a director who appears in at least one of these lists.

40 Paramount Pictures: http://www.paramount.com/inside-studio/studio/divisions Sony Pictures: http://www.sonypictures.com/corp/divisions.html The Walt Disney Company: https://thewaltdisneycompany.com/about/ 41 Twentieth Century Fox: https://en.wikipedia.org/wiki/20th_Century_Fox Universal Pictures: https://en.wikipedia.org/wiki/Universal_Pictures Warner Bros.: https://en.wikipedia.org/wiki/Category:Warner_Bros 42

7.2.6 Production and release time

Given that the data set contains films produced in the range of 11 years, the production years were controlled for in the analysis. The information on the release year of the film was considered important and was controlled for in the analysis, since market conditions may slightly differ over the years.

The information about release dates was used to identify films which were released in the summer and before Christmas. The summer release was defined as the first theatrical release in July or August and Christmas release as the first theatrical release in November or December.

8 Data limitations

There is limited availability of financial data on films, which represents a major shortcoming for the underlying analysis. So far, there is no alternative source of information which would systematically monitor film finance and give an overall picture of movies’ costs and revenue streams.

In the case of revenues, only theatrical box office is accounted for, meaning that other revenue sources such as TV licences, DVD sales, streaming, home-rentals etc. are not included. Therefore, the data do not represent the overall revenues of a film. On the other hand, theatrical box office is the basic success measure used in economic research, since it may determine the movie’s overall financial performance. The same applies to the data on the production budget. Film studios do not usually make marketing and advertising costs publicly available, therefore the IMDb data represent only the costs of the film’s production, without marketing expenditures.

Thus, by giving information about the proportion between the two measures, the revenue/budget ratio indicates how many times the movie covered its production costs. Although it can serve as a good approximation of profitability, it should not be interpreted as profitability per se. Moreover, the numbers on revenue/budget are only relative; hence they do not say anything about absolute profits or losses of a film.

Finally, it is generally the case that the more famous or successful a movie is, the more complete is the data in IMDb. This implies that the sample is not completely random, since one of the criteria used for including a film in the data set was the availability of revenue and budget data. The analysed sample thus contains rather better-grossing, better advertised movies or movies produced by big studios, and less low-budget movies produced by small studios. This, on the other hand, leads to an advantage in terms of reliability of other types of data (on film genres, writers, release dates etc.). The fact that the sample consists of rather more successful films means that this kind of information is more complete and trustworthy.

The study intended to include all key factors identified by the previous research; however, given the complexity of the analysed data the possibility of omitted variable bias must be admitted. Omitted variable bias is created when, due to leaving out an important explanatory variable, the model compensates for the loss by over- or underestimating the effect of one or more other factors. (Koop 2008, p. 96-97) 44

9 Descriptive statistics

1336 of 1457 films in the data set are produced by more than one producer. Out of these, 1095 are (international) co-productions, i.e. their producers have their seat in different countries. The most frequently occurring production country is the USA, since 732 films in the data set involve a US-based production studio.

In total, there are 362 national films either produced by a single studio or more producers. 577 of the films are produced exclusively by studios based in Europe.

At least one of the major studios is involved in the production of 622 films in the sample, most of which are co-productions. Out of solely European films, 201 observations involve a major producer (around one third), while out of the films produced in cooperation with a studio from non-EU country almost a half (421) involves one of the major studios.

The statistics on producers is summarized in the following chart. The numbers in brackets refer to the number of films in the respective subsample, where a major studio was involved in production:

Graph 1: Co-productions and producers (N=1457)

European national and co-productions with other countries

1457 (622)

National Co-productions films 1025 (523) 362 (99)

European Producer from non- 577 (201) EU country involved 880 (421)

National Co-productions Producer from USA 362 (99) 215 (102) involved 732 (385)

Giving an overview of countries of production for the whole data set is difficult, since most of the films are co-productions and many of them involve many production studios. The following graph shows the number of national films (i.e. without co-productions) produced in different European countries. More than 85 % of national films were produced in 7 countries: apart from the “Big 5” (UK, France, Spain, Italy and Germany), a big share of national films was produced by Dutch and Finnish studios:

Graph 2: National films from European countries (N=362)

National films from European countries

62 61 52 41 36 35 26

8 6 6 6 5 5 3 2 2 2 2 1 1

Almost 30 % of films in the data set are adaptations. In the subsample of purely European films the use of adapted content is relatively lower. The following table gives an overview of film characteristics with division between the whole sample and the sample of exclusively European films.

Table 1: Film characteristics

Characteristics N in the Share (%, the N European Share (%, among whole sample whole sample) films European) Adaptation 421 28.90 141 24.44 Adult 6 0.41 0 0.00 Action 282 19.35 61 10.57 Adventure 197 13.52 52 9.01 Animation 38 2.61 16 2.77 Biography 95 6.52 33 5.72 Comedy 504 34.59 221 38.30 Crime 284 19.49 96 16.64 Drama 860 59.03 340 58.93 Family 123 8.44 50 8.67 Fantasy 136 9.33 35 6.07 History 78 5.35 33 5.72 Horror 131 8.99 47 8.15 Musical 24 1.65 9 1.56 Mystery 158 10.84 48 8.32 Romance 324 22.24 109 18.89 Science-fiction 89 6.11 20 3.47 Short 13 0.89 4 0.69 Thriller 418 28.69 111 19.24 War 96 6.59 37 6.41 Western 10 0.69 2 0.35

168 films in the whole data set were first released in the summer months and 180 in the before-Christmas period. These numbers suggest a slightly lower competition in both of these seasons.

Not surprisingly, European films have relatively less Oscar nominations and employ top directors relatively less frequently. The following table contains information on the remaining binary variables used in the model:

Table 2: Oscar nominations, directors and release dates

N in the Share (%, the N European Share (%, among whole sample whole sample) films European) Oscar nomination 148 10.16 22 3.95 Director 113 7.76 26 4.50 Christmas release 180 12.35 73 13.11 Summer release 168 11.53 60 10.77

Films in the sample are rated on average between 6 and 7 stars. The mean and median values oscillate around the same values. The worst rated movie in the sample has 2 stars and the best rated film has 9 stars.

Graph 3: IMDb rating frequencies

A quick look at data on revenues and budget already suggests that most films in the sample do not recoup their costs of production. The median value of the budget variable is almost three times higher than the median value of box office revenues.

Table 3: Production budget and box-office (mil. USD)

N Median Mean St. dev. Min Max

Budget 1457 9 097.53 20 522.75 28 763.31 0.0047 200 644.70 - EU films 577 3 697.87 6 991.55 10 427.60 0.0047 91 764.79 Box office revenues 1457 3 810.86 39 809.24 98 162.15 0.7550 947 785.90 - EU films 577 1 061.54 5 838.75 16 471.25 0.7550 197 249.10

One can also observe differences between the values for the whole sample and for the subsample of purely European films. The latter seem to have considerably lower budgets and revenues. The reason might be the strong presence of Hollywood “Big Six” in the whole sample (349 observations), which appear to have considerably higher budgets. The EU-majors (336 observations) do not succeed to raise budgets as high as the Hollywood majors; however, the mean and median values of their production budgets seem to be much higher than the European average numbers.

For a correct understanding of the following graph and the distinction that is made between categories, it must be noted that a Big Six subsidiary might be involved in production of a purely European movie, the same as an EU-major might be involved in production of a film in cooperation with a non-EU based company.

Graph 4: Budget values for chosen categories (mil. USD)

Mean and median values of budget

Total

Big 6

EU films

EU major

0 10.000 20.000 30.000 40.000 50.000

Mean Median

Significant differences between median and mean values also confirm what was stated by the previous research – that box office and budget data is highly skewed and therefore far from having normal distribution. The following histograms illustrate the strong right-skew of distribution of budget and box office revenues.

Graphs 5 and 6: Budget and box office distribution

The table below shows statistics on the revenue-to-budget ratio, used here as an approximation of profitability. The median value is smaller than one, which means that most movies do not even cover their production costs. The difference between mean and median values is even bigger than for box office and budget data – profitability data is also right- skewed.

Table 4: Revenue/budget values

N Median Mean St. dev. Min Max

Revenue/budget 1457 0.56 147.15 4 447.12 0.0002 162 886.70 - EU films 577 0.32 284.11 6 780.99 0.0002 162 886.70

The following graph shows the share of films in the sample (and subsample of purely EU films), for which the revenue/budget ratio is superior to certain values. The first two columns show the share of films, which were able to cover their production budget with the box office revenues. The middle two are based on the data of MPAA for 2007, according to which the average advertising costs were about 50 % of the production budget. These two 50 columns thus show the share of films which were able to cover their production and advertising costs with their box office revenues. With a certain amount of simplification, the last columns show the share of “successful” films, based on the assumption that a film makes a meaningful profit, if its revenues reach twice the amount of production costs. (Escoffier and McKelvey 2015, p. 53)

Graph 7: Profitability

Revenue/budget ratio 45

38,44 40 35 29,41 30 25,85 25 22,86 20 17,24 13,11 15 10

% of films of % infilms the sample 5 0 Rev/bud >1 Rev/bud >1.5 Rev/bud >2

Whole data set EU films

10 Method

The data analysis has been run using the software package STATA 13. The multiple regression has been chosen as the method for the analysis. In general, the objective of the multiple regression analysis is to check whether the variation in the dependent variable can be explained by a set of factors. The model assumes a linear relationship between a dependent variable and multiple independent variables.

The multiple regression model with k explanatory variables is written as:

Yi = α + β1X1i + β2X2i + … + βkXki + εi

The ordinary least squares estimation (OLS) of the multiple regression model aims at identifying the best fitting line representing the linear relationship between Y and X1 - Xk. The ̂ ̂ objective is to choose such values of α̂ and β1 − β푘 that minimise the sum of squared residuals (SSR). According to the Gauss-Markov theorem, if the classical assumptions are met, OLS is the best linear unbiased estimator. The classical assumptions of the linear regression model are as follows:

1. E(εi) = 0. Mean zero errors. 2 2 2. var(εi) = E(εi ) = σ . Homoskedasticity.

3. cov(εi, εj) = 0 for i ≠ j. εi and εj are uncorrelated with one another.

4. εi is normally distributed.

5. X1i, …, Xki are fixed. They are not random variables. (Koop 2008, p. 91-95)

To measure the goodness of fit of the model to the data, the coefficient of determination (R2) is used, which can be interpreted as the proportion of the variability of the dependent variable that can be explained by the explanatory variables. The coefficient can be represented as follows:

∑ 2 2 푆푆푅 휀푖̂ 푅 = 1 − = 1 − 2 푇푆푆 ∑(푌푖 − 푌̅)

In the multiple linear regression, the adjusted coefficient of determination is also used in addition to the R2. The adjusted R2, unlike the regular R2, does not necessarily rise together with adding a new explanatory variable to the model, and thus it serves as a better measure of goodness of fit than R2. (Koop 2008, p. 91-95)

11 Data analysis

The following variables were used for the analysis:

Table 5: Variables used in the analysis

Variable Description logbous box office revenues in USD relative to the year 2000, logarithmic transformation logrevbud revenue-to-budget ration in USD relative to the year 2000, logarithmic transformation logbud budget in USD relative to the year 2000, logarithmic transformation adaptation dummy variable identifying films as adaptations genreadult genreaction dummy variables identifying films of certain genres genreadventure genreanimation genrebiography genrecomedy genrecrime genredrama genrefamily genrefantasy genrehistory genrehorror genremusical genremystery genreromance genrescifi genreshort genrethriller genrewar oscnomgenrewestern films which have at least one Oscar nomination rating average of IMDb user rating (between 1 and 10) major films produced by a major US or European studio crosscoprod international co-productions topdirector films directed by a well-known director sumrel films first released in July or August chrisrel films first released in November or December pr2001-pr2010 dummy variables to control for production years

Due to the highly right-skewed distribution of all financial data, logarithmic transformation has been used for revenues, revenue/budget ratio as well as for the production budget.

Before running the analysis, it was necessary to check the correlation between the variables to detect a possible multicollinearity problem. The correlation matrix, which is attached in Appendix 1, does not indicate any strong correlation.

11.1 Models and results

The tested hypotheses concern four main categories of variables: content, producer, director and release pattern. Therefore, four models were created, adding these categories to the model step by step. An additional model was created in order to show the effect of the factors on the revenue/budget ratio.

Given that the films come from different years, production years were controlled for. Oscar nomination and IMDb user rating were also used as control variables, to allow for comparison between films of similar quality. Furthermore, when co-production is added in the model, participation of a major studio in production is also controlled for. The aim of this approach is to distinguish between the effect of financial and negotiation power of a major and synergic effect stemming from the presence of an international element.

11.1.1 Regression analysis: full sample

The following table shows the results of the multiple linear regression. Only statistically significant genres are shown; however, to allow the analysis to account for a complete content characteristic of the films included in the sample, all genre types were left in the model. The variables of major interest are highlighted.

Table 6: Regression results – full sample

1 2 3 4 5 logbous logbous logbous logbous logrevbud logbud 0.8988*** 0.8075*** 0.8005*** 0.7929*** -0.2071** (0.0733) (0.0856) (0.0859) (0.0856) (0.0856) genreaction 0.6080*** 0.5796*** 0.5889*** 0.6163*** 0.6163*** (0.1635) (0.1593) (0.1596) (0.1610) (0.1610) genreanimation -0.5481 -0.6027* -0.5825* -0.5882* -0.5882* (0.3339) (0.3352) (0.3356) (0.3342) (0.3342) genrecomedy 0.6594*** 0.6164*** 0.6226*** 0.6437*** 0.6437*** (0.1346) (0.1340) (0.1338) (0.1335) (0.1335) genredrama -0.7453*** -0.7134*** -0.7215*** -0.6965*** -0.6965*** (0.1407) (0.1414) (0.1417) (0.1421) (0.1421) genrefamily 0.6106*** 0.6057*** 0.6203*** 0.5861*** 0.5861*** (0.1870) (0.1845) (0.1855) (0.1869) (0.1869) genrehorror 0.3539 0.4009* 0.4072* 0.4450** 0.4450** (0.2241) (0.2169) (0.2170) (0.2151) (0.2151) genrewar -0.3827* -0.2858 -0.2785 -0.2785 -0.2785 (0.2301) (0.2301) (0.2305) (0.2288) (0.2288) adaptation 0.3984*** 0.3509*** 0.3598*** 0.3401*** 0.3401*** (0.1217) (0.1201) (0.1208) (0.1209) (0.1209) oscnom 1.6988*** 1.6301*** 1.5911*** 1.5518*** 1.5518*** (0.1485) (0.1478) (0.1491) (0.1498) (0.1498) rating 0.4171*** 0.3663*** 0.3583*** 0.3630*** 0.3630*** (0.0602) (0.0598) (0.0599) (0.0598) (0.0598) crosscoprod 0.1631 0.1606 0.1923 0.1923 (0.1849) (0.1843) (0.1847) (0.1847) major 0.7245*** 0.7188*** 0.7275*** 0.7275*** (0.1235) (0.1232) (0.1228) (0.1228) topdirector 0.2918* 0.2763* 0.2763* (0.1598) (0.1587) (0.1587) sumrel -0.1014 -0.1014 (0.1794) (0.1794) chrisrel 0.4870*** 0.4870*** (0.1560) (0.1560) _cons -2.3208* -0.9686 -0.8409 -0.8187 -0.8187 (1.1979) (1.3317) (1.3394) (1.3346) (1.3346) years yes yes yes yes yes genres yes yes yes yes yes

N 1457 1457 1457 1457 1457 Adjusted R2 0.49 0.50 0.50 0.51 0.17 R2 0.50 0.52 0.52 0.52 0.19 Standard errors are presented in parentheses. *** p<0.01 ** p<0.05 * p<0.1 55

The results of the regression analysis confirm that films using adapting content perform significantly better in the movie theatres than wholly original films. Adaptation holds its strong significance in all the four models and the coefficient remains almost unchanged. The models suggest that the theatrical revenues of a film adapting pre-existing content are on average 34-40 % higher than in case of films based on an original screenplay, ceteris paribus.

As for genres, action, animation, comedy, drama, family, horror and war turned significant in at least one of the models. Genres action, comedy and family are strongly positively associated with theatrical revenues, while dramas contribute to the theatrical revenues negatively. An interesting change can be observed with adding the producer data into the model (model 2) – war turned insignificant, while horror and animation turned significant. In contrast to previous studies, horror was found to be positively associated with theatrical revenues. This can be due to the fact that film quality indicators were controlled for in all the models, which allows for examining the effect of the genre itself, holding the quality constant. Horror movies are, in general, considered to be of a lower quality as compared to other films. The results of this analysis, however, show that a horror movie ceteris paribus, i.e. having besides other things comparable budget, user rating and critics’ acceptance, performs 40-45 % better in cinemas then a film of a different genre.

The variable for co-production is insignificant in all models. The models thus do not show any evidence in favour of the hypothesis H3. However, the control variable major is highly significant. The results show that involving a major studio in production increases theatrical revenues by 72-73 %. As far as producers are concerned, it seems that the financial success of the films in the sample is driven by major producers, regardless of whether the film is co-produced or not.

All else being held equal, employing a popular director increases revenues by 28- 29 %, on average. This finding is in conformity with the previous research, which suggests that films directed by well-known figures are attractive to the European audience.

The model testing the release pattern shows that films released before Christmas perform 49 % better as compared to films released in other periods, ceteris paribus. On the contrary, the coefficient of films released in the summer is slightly negative but insignificant, which means the hypothesis on positive effect of summer release is not supported by the data. The big success of before-Christmas films might be driven by slightly decreased competition (as it was shown in descriptive statistics), combined with increased cinema attendance. 56

However, this conclusion cannot be drawn with certainty, as will be explained below.

At the end, an additional regression was run with revenue/budget ratio as the dependent variable. The model shows that this approximation of profitability is driven by the same factors as box office revenues, which is given by its definition based on revenues. However, the budget influence is negative in this case, which only confirms findings of numerous previous studies – high budget films have generally higher total revenues, but are less likely to become profitable.

Some other specifications were also tested to check the robustness of the models, for example adding a variable dividing production countries in categories, with comparable results.

11.1.2 Regression analysis: EU sample

Subsequently, the same set of regressions was run using the restricted sample of purely European films. This means that co-productions involving non-EU countries were excluded from the data set. The reason for this approach is the fact that the full sample results might be by a large part driven by presence of the US studios. The aim of this additional analysis is thus to examine the validity of results for purely EU films:

Table 7: Regression results – EU sample

1 2 3 4 5 logbous logbous logbous logbous logrevbud logbud 0.5327*** 0.5191*** 0.5065*** 0.4986*** -0.5014*** (0.0878) (0.1046) (0.1043) (0.1036) (0.1036) genreadventure 0.6167* 0.6580** 0.6714** 0.6760** 0.6760** (0.3322) (0.3268) (0.3257) (0.3282) (0.3282) genrebiography 0.4083 0.5611 0.6088* 0.6109* 0.6109* (0.3650) (0.3652) (0.3646) (0.3685) (0.3685) genrecomedy 0.8916*** 0.8476*** 0.8859*** 0.9085*** 0.9085*** (0.2069) (0.2042) (0.2014) (0.2016) (0.2016) genredrama -0.5721** -0.5101** -0.5205** -0.4911** -0.4911** (0.2233) (0.2201) (0.2188) (0.2216) (0.2216) genrefamily 0.6357** 0.6223** 0.6537** 0.6236** 0.6236** (0.2593) (0.2651) (0.2642) (0.2621) (0.2621) genremusical 0.6231 0.7934* 0.8213* 0.8574* 0.8574* (0.4039) (0.4318) (0.4287) (0.4396) (0.4396) adaptation 0.4254** 0.4173** 0.4558** 0.4508** 0.4508** (0.2071) (0.2019) (0.2021) (0.2028) (0.2028) oscnom 1.5996*** 1.4992*** 1.6192*** 1.5969*** 1.5969*** (0.4824) (0.4911) (0.4945) (0.4924) (0.4924) rating 0.2878*** 0.3031*** 0.2746*** 0.2796*** 0.2796*** (0.0913) (0.0933) (0.0929) (0.0929) (0.0929) crosscoprod -0.6081*** -0.6345*** -0.6114*** -0.6114*** (0.2314) (0.2300) (0.2309) (0.2309) major 0.5951*** 0.5522*** 0.5285** 0.5285** (0.2133) (0.2132) (0.2129) (0.2129) topdirector 1.0891*** 1.1014*** 1.1014*** (0.4011) (0.3995) (0.3995) sumrel -0.4620 -0.4620 (0.3084) (0.3084) chrisrel 0.2705 0.2705 (0.2499) (0.2499) _cons 2.9800** 3.1405* 3.3955** 3.5360** 3.5360** (1.4101) (1.6681) (1.6689) (1.6657) (1.6657) genres yes yes yes yes yes years yes yes yes yes yes N 577 577 577 577 577 Adjusted R2 0.25 0.27 0.28 0.28 0.17 R2 0.29 0.31 0.32 0.33 0.23 Standard errors are presented in parentheses. *** p<0.01 ** p<0.05 * p<0.1

Results for the adaptation hypothesis are consistent with the findings of the full- sample analysis. The same applies to genres comedy and family, which remain strongly positively associated with revenues, and drama, which influences revenues negatively. A change can be observed in the case of action films, which appear to be irrelevant in the restricted sample. On the contrary, adventure films are significantly positively associated with revenues, increasing them by 62-68 % as compared to non-adventure films. In some of the models, biography and musical were identified as significant, with positive coefficients.

A change can be observed in the results for co-productions. In the restricted sample, co-production variable is significant at 0.01 level and the coefficient is negative. This means that a purely European co-production performs significantly worse than a European national film, ceteris paribus. This result is surprising, given that common sense would suggest that co-producers should be able to achieve greater financial success together.

To interpret the results better, the difference in budget distributions of co-productions and national films in the restricted sample was tested using the Non-parametric equality-of- medians test implemented in STATA 13. The null hypothesis of the test is that both sets of data come from the samples which were drawn from populations with the same median. (STATA 2017, Ranksum) According to the table below, the null hypothesis is clearly rejected, meaning that the median values of budget of co-productions and national films differ significantly.

Table 8: Non-parametric equality-of-medians test

Greater than the median crosscoprod Total 0 1 No 216 73 289 Yes 146 142 288 Total 362 215 577 Pearson chi2(1) = 35.6785 Pr = 0.000 Continuity corrected: Pearson chi2(1) = 34.6573 Pr = 0.000

For a better illustration, the table below shows the mean and median values of budget for both groups of observations. A high difference between mean and median values can be observed:

Table 9: Budget values of co-productions and national films (mil. USD)

N Median Mean St. dev. Min Max

Budget co-production 215 5494.38 9801.20 12740.04 0.0047 91764.79 Budget national 362 2882.03 5313.99 8354.80 .0047382 0.0786 72756.16 91764.79 16

Based on this, it can be concluded that in European conditions, co-productions, as assumed, have in general higher budgets. However, keeping the budget on the same level, they do not manage to outperform national films. The explanation cannot lie in the low quality of co-produced films, because quality indicators are controlled for in the models. One of the possible explanations could be the nature of co-produced films: co-productions might often be experimental films or films that target narrow audiences. The producers can use this strategy to raise financing and get access to national support schemes, because otherwise they might not be able to raise enough funds at all. The final product can even be of a decent quality, but does not earn enough in movie theatres, since these types of films do not aspire at becoming blockbusters.

To differentiate between the impact of the size of budget on co-produced and national films, an interaction of budget and co-production was added, but it proved to be highly insignificant, and therefore it was removed from the final models.

The models testing a famous director confirm even stronger the theory that European film market is driven by the fame of directors. The coefficient of top director is significant at 1 % significance level and indicates that European films employing a famous director perform more than twice better than other films, ceteris paribus.

Hypothesis H5 regarding release pattern, can be rejected based on the fourth model. In purely European conditions, summer or Christmas release is irrelevant. This difference between the full and restricted sample might be due to the fact that in the full sample there are numerous films co-produced with big US production companies, i.e. there is a higher share of “blockbusters”. The results thus might be driven only by the strategy of this sub-group of producers to release films before Christmas.

Finally, the last model confirms the findings from the full sample: influencers of profitability do not differ from influencers of revenues, but the size of budget has a significant

60 negative impact on profitability.

The adjusted R2 of the regressions is, compared to other similar studies, relatively high in the full sample. Between 49 and 51 % of variability in the data is explained by the models. In case of the restricted sample, the numbers are much lower (25-28 %). The reason for such low values is high heterogeneity in the data, which is difficult to capture with limited set of observatory variables that can be used in the model. Obviously, there are other important factors that influence the film success that are difficult to operationalise and control for.

In this thesis, films are treated as business projects. Therefore, if we consider a film to be an investment (given by its production budget) and theatrical revenues a rough approximation of total revenues in the first months after release, we can depict short-term profitability as a function of budget. Based on this reasoning, the graph below presents some illustrative examples of how the producer’s strategy can influence the profitability of a film, defined here as the ratio of revenue/budget. It shows predicted profitability for different levels of budget, taking into account chosen film specifications. The predictions are based on the results obtained when estimating Model 3 on the restricted sample.

Profitability was defined as the ratio of revenue/budget. Where the value is higher than 1, the film is able to cover its production costs with the box-office revenues. Depicted values of budget represent the value of the 10th to 90th percentile and the maximum budget value in the restricted sample.

The graph provides a more comprehensive view of the relationship between profitability and budget by varying certain film characteristics, namely genre, screenplay base and production partner. Other specifications were held equal. The aim was to show an example of a “regular” film and illustrate graphically the effect of the producer’s decisions. Therefore, all films depicted below are produced by a small studio (i.e. not a major), have no Oscar nominations, do not employ a well-known director and were produced in 2010. As for IMDb rating, an average rating for drama (6.6) and for comedy (6.1) was used in calculations.

The lowest line represents a co-produced drama based on an original screenplay. The others illustrate the change in predicted profitability when certain specifications are altered. It is necessary to note that not accounting for other sources of revenues leads to under-valuation of the overall predicted profitability. Therefore, rather than the nominal values of profitability, the focus should be the shape of the curves and the changes in their position with each

61 modification of film attributes.

All modifications accounted for in the example – i.e. changing the genre from drama to comedy, co-production into a national film and original screenplay into adaptation – move the line higher on the profitability scale. A big change can be observed when the film uses adapted content. As expected, in all cases the trend is clearly descending, which is caused by the negative relationship between the size of the budget and profitability. For the 10 % most expensive films the predicted short-term profitability is lower than 0,4 in all cases. In the case of films based on an original screenplay, it does not exceed 0,1.

Graph 8: Short-term profitability as a function of budget

Short-term profitability as a function of budget 1,6

1,4

1,2

0,8

Profitability 0,6

0,4

0,2

0 10th 20th 30th 40th 50th 60th 70th 80th 90th max Budget values - percentiles

National comedy based on a novel National drama based on a novel National comedy based on original screenplay National drama based on original screenplay Co-produced comedy based on original screenplay Co-produced drama based on original screenplay

12 Discussion and recommendations

In terms of film content, the data analysis proves the adaptation strategy is a very advantageous one. A story built on a well-known concept clearly attracts audiences to the cinemas and increases theatrical revenues of the film. In general, films targeting broad audiences perform better than other films. Genres action, adventure, comedy and family were identified as strong influencers of theatrical revenues. These genres are generally broadly popular, family-friendly and no restrictions are usually placed on their screening. This suggests that producing a film of such nature can be a good risk-mitigation strategy. In contrast, films whose theatrical attendance is more likely to be restricted (like dramas or war movies) influence revenues negatively.

A strategy based on designing content so that it fits to the taste of movie-goers does not require strong market position or big financial resources; therefore, it can be an effective way of increasing the probability of success for small producers.

Attracting a partner from a foreign country appears to be a clever way to raise finance. In European conditions, co-productions are characterized by higher budgets; however, the co- producers do not manage to shoot more successful films with them. A hypothesis drawn from this fact suggests that co-productions are generally films that do not target masses. This hypothesis might serve as a basis for future research, which could focus on a more in-depth analysis of co-productions performance, by focusing on the features and specificities of co- produced films.

The analysis confirmed the leading role of directors in the European film market. The presence of a famous director serves as a signalling property to the audience. It can advise them on the film’s quality before they see it and facilitate their decision on whether or not to go to the cinema. The influence of stars on the success of European films was left aside for the concerns of undue subjectivity. However, under conditions of better data availability, it may also be an interesting possible research topic.

The results on the effectiveness of choosing the period of release are not conclusive. It is suggested that it might be advantageous to release the film before Christmas; however, the analysis does not give any clear answer concerning the rationale behind it. This result can be given by the decreased competition combined with increased cinema attendance. However, the result may well be driven by the fact that it is rather big film studios producing 63 mainstream films which release movies before Christmas. Therefore, it is not possible to draw a clear conclusion with regards to the strategy of choosing release pattern.

The explanatory power of the models might be enhanced especially by including the data on advertising expenditures. Due to the lack of publicly available data the impact of production budget and marketing expenditures could not be examined in this thesis. The separated effects of these two types of expenditures can be another possible topic of future research.

13 Concluding remarks

The aim of the thesis was to identify the determinants of box-office success of films produced in the European Union in the years 2000-2010. Due to the high-risk profile of the film industry, possible risk-mitigation strategies were identified, and their effectiveness was empirically examined.

In the first part, the characteristics of the industry were presented, with focus on the riskiness of movie production. Based on the literature review, possible strategies of risk mitigation were suggested. Subsequently, the source of data and the data base used for analysis were presented, and linear regression analysis was run with the objective to test the hypotheses.

Four out of five hypotheses were confirmed – film producers can, to a certain extent, influence the success of their project by choosing wisely the genre, by adapting a well-known story or employing a famous figure as director. Co-production strategy was found to be an effective way of ensuring enough finance. These results can be used in practice especially by film producers who seek information on consumer preferences. Knowledge gained from the analysis can serve as an input for the decision-making process when determining the features of their future project.

The novelty of the thesis consists in the territorial scope of the analysis. Unlike previous studies, which focused mostly on the US market, this study is, to the best of the author’s knowledge, the only one focused on the whole territory of the European Union. Its contribution to the current state-of-art is thus immense, since it allows comparing the results of previous research with results under conditions of the film market of the European Union.

14 List of references

Articles:

[1] AAKER, D. A. and KELLER, K. L. Consumer evaluations of brand extensions. Journal of Marketing. Jan 1990. 54 (1), p. 27-41. ISSN 0022-2429.

[2] BAGELLA, M. and BECCHETTI, L. The Determinants of Motion Picture Box Office Performance: Evidence from Movies Produced in Italy. Journal of Cultural Economics. Nov 1999. 23 (4), p. 237-256. ISSN 0885-2545.

[3] CHANG, B. H. AND KI, E. J. Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property. Journal of Media Economics. 2005. 18(4), p. 247-269. ISSN 0899- 7764.

[4] DE VANY, A. and WALLS, W. D. Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office? Journal of Cultural Economics. Nov 1999. 23 (4), p. 285-318. ISSN 0885-2545.

[5] DUAN, W. et al. Do Online Reviews Matter? – An Empirical Investigation of Panel Data. Decision Support Systems. Nov 2008. 45(4), p. 1007-1016. ISSN 0167-9236.

[6] ELBERSE, A. and ELIASHBERG, J. Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures. Marketing Science. 2003. 22(3), p. 329-354. ISSN 0732-2399.

[7] ELLIOT, C. and SIMMONS, R. Determinants of UK Box Office Success: The Impact of Quality Signals. Review of Industrial Organization. Sep 2008. 33(2), p. 93-111. ISSN 0889-938X.

[8] ESCOFFIER, N. and McKELVEY B. The Wisdom of Crowds in the Movie Industry: Towards New Solutions to Reduce Uncertainties. International Journal of Arts Management. Dec 2015. 17(2), p. 52-63. ISSN 1480-8986.

[9] GHIASSI, M. et al. Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Systems with Applications. Apr 2015. 42(6), p. 3176–3193. ISSN 0957-4174.

[10] HASBROUCK, R. B. and DENIZ, B. When To Greenlight: Examining the Pre-release Factors that Determine Future Box Office Success of a Movie in the United States. International Journal of Economics and Management Sciences. 2012. 2(3). p. 35-42. ISSN 2162-6359.

[11] IACOBUCCI, D. et al. Dynamic Effects Among Movie Ratings, Movie Revenues, and Viewer Satisfaction. Journal of Marketing. Jan 2010. 74 (1), p. 108-121. ISSN 0022-2429.

[12] JOSHI A. and MAO H. Adapting to succeed? Leveraging the brand equity of best sellers to succeed at the box office. Journal of the Academy of Marketing Science. Jul 2012. 40(4), p. 558-571. ISSN 0092-0703.

[13] LAMPEL, S. and SHAMSIE, S. Critical Push: Strategies for Creating Momentum in the Motion Picture Industry. Journal of Management. 2000. 26(2). p. 233-257. ISSN 0149-2063.

[14] LITMAN, Barry R. Predicting success of theatrical movies: An empirical study. The Journal of Popular Culture. Sept 2013. 16(4). p. 159-175. ISSN 0022-3840. First published in 1983.

[15] LIU, A. et al. Star power in the eye of the beholder: A study of the influence of stars in the movie industry. Marketing Letters. Dec 2014. 25(4). p. 385-396. ISSN 0923-0645.

[16] PANGAKER, N. A. and SMIT, E. v. d. M. The determinants of box office performance in the film industry revisited. South African Journal of Business Management. Sept 2013. 44(3). p. 47-70. ISSN 2078-5976.

[17] PINE, B. J. and GILMORE, J. H. Welcome to the Experience Economy. Harvard Business Review. Jul 66

1998. 76(4), p. 97-105. ISSN: 0017-8012.

[18] PRAG, J. and CASAVANT, J. An Empirical Study of the Determinants of Revenues and Marketing Expenditures in the Motion Picture Industry. Journal of Cultural Economics. Nov 1999. 18 (3), p. 217-235. ISSN 0885-2545.

[19] SHARDA, R. and DELEN D. Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications. Feb 2006. 30(2), p. 243–254. ISSN 0957-4174.

[20] TERRY, N. et al. The Determinants of Foreign Box Office Revenue for English Language Movies. Journal of International Business and Cultural Studies. 2010. 2(1). p. 117-127. ISSN 2327-5391.

Articles in online journals:

[21] CHINTAGUNTA, P. K. et al. The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets. Marketing Science. SSRN [online]. Posted: 23 Jan 2009. [cit. 24.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1331124

[22] GMEREK, Natalia. The determinants of Polish movies’ box office performance in Poland. Journal of Marketing and Consumer Behaviour in Emerging Markets. [online]. 2015. 01(01) [cit. 19.03.2017]. p. 15-35. ISSN 2449-6634. Available from: http://www.wz.uw.edu.pl/portaleFiles/5708-journal-of- m/no_1/JMCBEM_1(1)2015_Gmerek.pdf

[23] HADIDA, Allègre L. Motion picture performance: A review and research agenda. International Journal of Management Reviews. [online]. Sept 2009. 11 (3) [cit. 10.03.2017]. p. 247-360. ISSN 1468-2370. Available from: http://onlinelibrary.wiley.com/doi/10.1111/j.1468-2370.2008.00240.x/full

[24] HENNING-THURAU, T. et al. Determinants of motion picture box office and profitability: an interrelationship approach. Review of Managerial Science [online]. 2006. 01(01) [cit. 10.03.2017]. p. 65-92. ISSN 1863-6691. Available from: http://rd.springer.com/article/10.1007/s11846-007-0003-9

[25] HYATT, A. and JOHNSON, D. K. N. Why Do You Like What You Like? The Impact of Previous Netflix User Ratings on Subsequent User Experience. SSRN [online]. Posted: 12 Jul 2016 [cit. 07.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2807475

[26] KARNIOUCHINA, Ekaterina. Impact of Star and Movie Buzz on Motion Picture Distribution and Box Office Revenue. SSRN [online]. Posted: 9 Oct 2010 [cit. 07.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1689294

[27] KARNIOUCHINA, E. et al. A Note on Revenue versus Profitability as Indicators of Motion Picture Performance. SSRN [online]. Posted: 21 Nov 2010 [cit. 30.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1712088

[28] KIM, Myeong Hwan. Determinants of revenues in the motion picture industry. Applied Economics Letters [online]. 2013. 20(11) [cit. 07.03.2017]. p. 1071-1075. ISSN 1466-4291. Available from: http://www.tandfonline.com/doi/full/10.1080/13504851.2013.783678

[29] LEE, Y. et al. The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales. SSRN [online]. Posted: 23 May 2015 [cit. 07.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2609500

[30] LIU, Yong. Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. SSRN [online]. Posted: 26 Oct 2011 [cit. 07.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1949819

[31] LUO, Hong. When to sell your idea: Theory and evidence from the movie industry. Management 67

Science. [online]. 2014. 60(12) [cit. 29.04.2017]. p. 3067-3086. ISSN 1526-5501. Available from: http://pubsonline.informs.org/doi/abs/10.1287/mnsc.2014.1983

[32] ORLOV, D. Y. and OZHEGOV E. M. Estimating the Efficiency of Sequels in the Film Industry. SSRN [online]. Posted: 25 Jun 2015. [cit. 24.03.2017]. ISSN 1556-5068. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2622493

[33] PELTONIEMI, Mirva. Cultural Industries: Product–Market Characteristics, Management Challenges and Industry Dynamics. International Journal of Management Reviews. [online]. Jan 2015. 17 (1) [cit. 24.03.2017]. p. 41-68. ISSN 1468-2370. Available from: http://onlinelibrary.wiley.com/doi/10.1111/ijmr.12036/full

[34] TETI, Emanuele. The dark side of the movie. The difficult balance between risk and return. Management Decision. [online]. 2013. 55 (4) [cit. 24.03.2017]. p. 730 - 741. ISSN 0025-1747. Available from: http://www.emeraldinsight.com/doi/full/10.1108/00251741311326536

Articles published on websites:

[35] BOCCARDELLI, P. et al. What is critical to success in the movie industry? A study on key success factors in the Italian motion picture industry. In: DIME Working Papers on Intellectual Property Rights [online]. April 2008. Working paper, no 48. [cit. 19.03.2017]. Available from: http://www.dime-eu.org/working- papers/wp14/46

[36] BOMSEL, O. and CHAMARET, C. Rentabilité des investissements dans les films français: Note de recherche. In: Centre de Recherche en Gestion: École Polytechnique [online]. Oct 2008. [cit. 24.03.2017]. Available from: http://crg.polytechnique.fr/fichiers/crg/perso/fichiers/chamaret_728_RentabContango2.pdf

[37] BOZDOGAN, Jasemin. The Paradox of French Motion Picture Industry, Hollywood in France: Over paid stars, Growing budgets, Profitability, The Perversive effect of Regulatory Subsidy System. In: Universidad de Valladolid [online]. 2016. [cit. 19.03.2017]. Available from: http://eventos.uva.es/file_manager/get_paper/6255

[38] DE VANY, A. and WALLS, W. D. Momentum, Motion Picture Profit, and the Curse of the Superstar. In: iBrarian [online]. 2002. [cit. 24.03.2017]. Available from: http://bit.ly/2nPNbg6

[39] RAVID, S. Abraham. Information, Blockbusters and Stars? A Study of the Film Industry. In: Department of Finance – Working Paper Series 1998 [online]. Nov 1997. [cit. 10.03.2017]. Available from: https://archive.nyu.edu/handle/2451/27020

[40] SAYEH, W. and BELLIER, A. Neural networks versus logistic regression: the best accuracy in predicting credit rationing decision. In: 32èmes Journées Internationales Monnaie Banque Finance: Nice Sophia-Antipolis, [online]. Jun 2015. [cit. 24.03.2017]. Available from: https://gdrenice2015.sciencesconf.org/53551/document

[41] SPARROW, I. R. and SIMONOFF, J. S. Predicting movie grosses: Winners and losers, blockbusters and sleepers. In: IOMS: Statistics Working Papers [online]. 1999. [cit. 10.03.2017]. Available from: https://archive.nyu.edu/handle/2451/14752

[42] TOPF, Pat. Examining Success in the Motion Picture Industry. In: The Park Place Economist [online]. 2010. 18(01) [cit. 19.03.2017]. Available from: http://digitalcommons.iwu.edu/cgi/viewcontent.cgi?article=1337&context=parkplace

[43] WEBLEY, P. and LEA, S. Topic 3: Path Analysis. In: University of Exeter: Department of Psychology [online]. 1997. [cit. 19.03.2017]. Available from: https://people.exeter.ac.uk/SEGLea/multvar2/pathanal.html

[44] WUTZ J. and PÉREZ, V. Dissemination of European cinema in the European Union and the international market. In: Notre Europe: Institut Jacques Delors [online]. Nov 2017. [cit. 30.03.2017]. Available 68 from: http://www.notre-europe.eu/media/disseminationeuropeanfilm-wutzperez-ne-jdi-ifa-unifr- nov14.pdf?pdf=ok

Websites:

[45] Box Office Mojo, 2017. About Box Office Mojo. Boxofficemojo.com [online]. Box Office Mojo. [Cit. 19.03.2017]. Available from: http://www.boxofficemojo.com/about/?ref=ft

[46] Box Office Mojo, 2017. Box Office Tracking by Time. Boxofficemojo.com [online]. Box Office Mojo. [Cit. 19.03.2017]. Available from: http://www.boxofficemojo.com/about/boxoffice.htm

[47] Briefing. 2014. An overview of Europe’s film industry. Europarl.europa.eu [online]. European Parliament. [Cit. 19.03.2017]. Available from: http://bit.ly/2mG4yLv

[48] Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions. 2014. European Film in the Digital Era: Bridging Cultural Diversity and Competitiveness. Eur-lex.europa.eu [online]. European Commission. [Cit. 19.03.2017]. Available from: http://eur-lex.europa.eu/legal-content/CS/TXT/?uri=CELEX%3A52014DC0272

[49] Entertainment Weekly, 1996. The 50 Greatest Directors and their 100 Best Movies. Ew.com [online]. Time Inc. [Cit. 23.04.2017]. Available from: http://ew.com/article/1996/04/19/50-greatest-directors-and-their- 100-best-movies/

[50] European Audiovisual Observatory. 2015. The Circulation of European Films Outside Europe. Obs.coe.int [online]. European Audiovisual Observatory. [Cit. 19.03.2017]. Available from: http://bit.ly/2mihzPY

[51] European Audiovisual Observatory. 2016. How Do Films Circulate on VOD Services and in Cinemas in the European Union? Obs.coe.int [online]. European Audiovisual Observatory. [Cit. 19.03.2017]. Available from: http://bit.ly/28OUX2C

[52] Goodreads, 2017. About Goodreads. Goodreads.com [online]. Goodreads. [Cit. 31.03.2017]. Available from: https://www.goodreads.com/about/us

[53] IMDb, 2010. The Top Directors of the Last 20 Years. IMDb.com [online]. The Internet Movie Database. [Cit. 23.04.2017]. Available from: http://www.imdb.com/list/ls000000757/

[54] IMDb, 2012. Top 250 Directors by IMDb Rating. IMDb.com [online]. The Internet Movie Database. [Cit. 23.04.2017]. Available from: http://www.imdb.com/list/ls008344500/

[55] IMDb, 2017. About IMDb. IMDb.com [online]. The Internet Movie Database. [Cit. 19.03.2017]. Available from: http://www.imdb.com/pressroom/

[56] IMDb, 2017. IMDb New Title Submission Top Frequently Asked Questions. IMDb.com [online]. The Internet Movie Database. [Cit. 19.03.2017]. Available from: http://www.imdb.com/help/search?domain=helpdesk_faq&index=1&file=titletopfaq&ref_=hlp_sr_3

[57] IMDb, 2017. What kind of titles/films are eligible for addition? And what kind of proof is required? IMDb.com [online]. The Internet Movie Database. [Cit. 19.03.2017]. Available from: http://www.imdb.com/help/show_leaf?titleeligibility

[58] JRC Technical Reports. 2012. Statistical, Ecosystems and Competitiveness Analysis of the Media and Content Industries: The Film Sector. The Joint Research Centre [online]. European Commission. [Cit. 19.03.2017]. Available from: http://ftp.jrc.es/EURdoc/JRC69525.pdf

[59] OANDA Corporation, 2017. Currency Converter. OANDA [online]. OANDA Corporation. [Cit. 19.04.2017]. Available from: https://www.oanda.com/currency/converter/

[60] Paramount Pictures, 2017. Divisions. Paramount Pictures [online]. Paramount Pictures. [Cit. 03.04.2017]. Available from: http://www.paramount.com/inside-studio/studio/divisions

[61] Sony Pictures, 2017. Divisions. Sony Pictures [online]. Sony Pictures. [Cit. 03.04.2017]. Available from: http://www.sonypictures.com/corp/divisions.html

[62] STATA, 2017. Ranksum. STATA: Data analysis and statistical software [online]. StataCorp LLC. [Cit. 07.05.2017]. Available from: http://www.stata.com/manuals14/rranksum.pdf

[63] The Guardian, 2003. The World’s 40 Best Directors. The Guardian [online]. Guardian News and Media Limited. [Cit. 23.04.2017]. Available from: https://www.theguardian.com/film/2003/nov/14/1

[64] The Walt Disney Company, 2017. About The Walt Disney Company. The Walt Disney Company [online]. The Walt Disney Company. [Cit. 03.04.2017]. Available from: https://thewaltdisneycompany.com/about/

[65] US Bureau of Labor Statistics, 2017. Consumer Price Index Data from 1913 to 2017. US Inflation Calculator [online]. US Bureau of Labor Statistics. [Cit. 03.04.2017]. Available from: http://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from- 1913-to-2008/

[66] Wikipedia, 2017. Universal Pictures. Wikipedia: The Free Encyclopaedia [online]. Wikipedia. [Cit. 03.04.2017]. Available from: https://en.wikipedia.org/wiki/Universal_Pictures

[67] Wikipedia, 2017. Category: Warner Bros. Wikipedia: The Free Encyclopaedia [online]. Wikipedia. [Cit. 03.04.2017]. Available from: https://en.wikipedia.org/wiki/Category:Warner_Bros.

[68] Wikipedia, 2017. 20th Century Fox. Wikipedia: The Free Encyclopaedia [online]. Wikipedia. [Cit. 03.04.2017]. Available from: https://en.wikipedia.org/wiki/20th_Century_Fox

Monographies

[69] FINNEY, Angus, 2015. The International Film Business. A Market Guide Beyond Hollywood. 2nd ed. Oxon: Routledge. 287 p. ISBN 978-0-415-53153-5.

[70] GOLDMAN, William, 1984. The Adventures in the Screen Trade. 1st ed. Warner Books: New York. 594 p. ISBN: 0-446-39117-4.

[71] KOOP, Gary, 2008. Introduction to Econometrics. John Wiley and Sons Ltd: Chichester. 384 p. ISBN: 978-0-470-03270-1.

Judgements

[72] Judgement of the Court of Appeal of England and Wales of 20 February 1963 in case [1963] Ch. 587, Francis Day and Hunter Ltd. And Another v Bron and Another.

[73] Judgement of the House of Lords of 21 January 1964 in case [1964] 1 W.L.R. 273, Ladbroke (Football) Ltd. v William Hill (Football) Ltd.

[74] Judgement of the United States Court of Appeals (Second Circuit) of 13 November 1995 in case 71 F.3d 996, Knitwaves, Inc. v Lollytogs Ltd., Inc.

15 List of Shortcuts

MPAA – Motion Picture Association of America

IMDb – Internet Movie Database

RoI – Return on Investment

16 List of graphs and tables

Graph 1: Co-productions and producers

Graph 2: National films from European countries

Graph 3: IMDb rating frequencies

Graph 4: Budget values for chosen categories

Graphs 5 and 6: Budget and box office distribution

Graph 7: Profitability

Graph 8: Short-term profitability as a function of budget

Table 1: Film characteristics

Table 2: Oscar nominations, directors and release dates

Table 3: Production budget and box-office

Table 4: Revenue/budget values

Table 5: Variables used in the analysis

Table 6: Regression results – full sample

Table 7: Regression results – EU sample

Table 8: Non-parametric equality-of-medians test

Table 9: Budget values of co-productions and national films

17 List of Appendices

Appendix 1: Correlation matrix