Start Me Up: New Entry of Record Labels in the Music Industry

Master Thesis

MSc Business Administration

Management and Entrepreneurship in the Creative Industries

Faculty of Economics and Business, Amsterdam Business School

Myra Alice Wilhelmina Ruers - 11903929

Supervisor: Dr. M. Piazzai

21st of June 2018 Statement of Originality

This document is written by Student Myra Ruers, who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

2 Abstract

The music industry is extremely vibrant, diverse, and inherently uncertain. Record labels are the main intermediaries in this industry, facilitating the connection between the creative input from musicians and the demand from the consumers. Due to many technological advancements, the barriers to entry have decreased significantly, hence making it easier to enter this industry as a new record label. Additionally, strategic integration options allow existing firms to start new ventures by acquiring or initiating subsidiaries. This research focuses on the multiple modes through which new entry can be commenced. These entry modes all have their own characteristics and consequences for the configurations of the industry. The likelihood of entry, the subsequent performance of new record labels, and the effects on the diversity of the output are the main topics that are explored. In this study, databases storing longitudinal information on musical releases are used to compile a dataset with the necessary information on new record labels. The main outcomes reveal that there are still many organizations entering the industry despite a general trend towards disintermediation, and that the widely accepted categorization that divides record labels into two groups (major and independent) is not completely accurate, because large independent labels tend to act similarly to major labels.

3 Table of Contents Statement of Originality ...... 2 Abstract ...... 3 Table of Contents ...... 4 1. Introduction ...... 6 2. Literature Review ...... 9 2.1 Strategy and New Entry ...... 9 2.2 Strategic Integration ...... 11 2.3 The Music Industry ...... 12 2.4 Majors vs. Indies ...... 15 2.5 New Entry in the Music Industry ...... 17 2.6 Diversity in the Music Industry ...... 22 2.7 Vertical Integration in the Music Industry ...... 24 3. Data and Methods ...... 27 3.1 MusicBrainz Database ...... 27 3.2 Sample ...... 27 3.3 Variables ...... 29 3.3.1 Hypothesis 1...... 29 3.3.2 Hypothesis 2...... 30 3.3.3 Hypothesis 3...... 31 4. Results ...... 32 4.1 Hypothesis 1 ...... 32 4.1.1 Additional Analysis: Long-term and Short-term ...... 34 4.1.2 Additional Analysis: Electronic Music ...... 36 4.1.3 Additional Analysis: Billboard Chart ...... 36 4.2 Hypothesis 2 ...... 37 4.2.1 Additional Analysis: Original Grouping ...... 38 4.3 Hypothesis 3 ...... 39 5. Discussion ...... 40 5.1 De Novo Entrants ...... 40 5.2 De Ipso Entrants ...... 41 5.3 Internal Competition ...... 43 5.4 Access to Valuable Connections ...... 45 6. Conclusion ...... 48 6.1 Insights ...... 48 6.2 Practical Implications ...... 49 6.3 Limitations ...... 50 6.4 Future Research Options ...... 52 7. References ...... 53 8. Appendices ...... 58 8.1 Appendix 1 – Multidivisional Majors ...... 58 8.2 Appendix 2 – Lifecycle of Small Labels ...... 59 8.3 Appendix 3 – MusicBrainz Keys ...... 60 8.4 Appendix 4 – Statistical Analysis Results ...... 61 8.4.1 Normality tests ...... 61 8.4.2 Hypothesis 1a ...... 61

4 8.4.3 Hypothesis 1b...... 61 8.4.4 Hypothesis 1c ...... 62 8.4.5 Hypothesis 1d...... 63 8.4.6 Additional analysis H1: Short-term and Long-term ...... 63 8.4.7 Additional analysis H1: No Electronic ...... 64 8.4.8 Additional analysis H1: Billboard Songs and Weeks ...... 65 8.4.9 Hypothesis 2...... 66 8.4.10 Additional analysis H2: Original Grouping ...... 67 8.4.11 Hypothesis 3a ...... 68 8.4.12 Hypothesis 3b...... 68

List of Figures and Tables Figure 1. Music Industry Value Chain (Bockstedt et al., 2006) ...... 13 Table 1. Overview of hypotheses……..………………………………………………………………26 Table 2. Descriptives H1b …………………………………………………………………………... 32 Table 3. Results one-way ANOVA …………………………………………………………………. 33 Figure 2. Short-term and long-term means per label type ...... 35 Table 4. Descriptives Billboard songs and weeks ……………………………………………………37 Table 5. Results independent samples t-test ………………………………………………………… 38 Table 6. Descriptives H2 with original grouping …………………………………………………… 38 Table 7. Descriptives H3 ……………………………………………………………………………. 39

5 1. Introduction The creative industries are plagued by the tension between art and commerce (Caves,

2000). In the music industry, this distinction is also apparent. The art side, which consists of the artists and musicians who want to follow their heart and make the music they love, is contrasted against the commerce side, which consists of the businesses that are trying to fulfil corporate goals. These conflicting objectives create pressure on both parties and make fruitful collaboration often harder to achieve than in other industries. Additional factors that complicate the cooperation within the music industry, are the persistent oversupply and the inherent uncertainty that are present in the creative industries (Hirsch, 1972). This implies that there are many more songs and albums released than there is demand from consumers. This leads to extreme uncertainty about the chances of success for artists and bands (Peltoniemi, 2015).

The music industry configurations and relationships are extremely interesting to study because the environment is very vibrant. A lot of research has already been undertaken about the music industry in the past years (Burnett, 1996; Negus, 1999b; Lopes, 1992; Dowd, 2004a).

Despite these efforts, literature concerning this industry from a business perspective has largely focused on the beginning and the end of the value chain, namely the artists and the consumers.

The intermediaries that connect these two ends are often overlooked. The main intermediary in this industry is the record label. Record labels are extremely important actors in the music industry value chain, as they are responsible for discovering and developing great artists. They also arrange the publishing, marketing, and licensing of the musical recordings from the artists they represent (Bockstedt, Kauffman & Riggins, 2006). According to Hirsch (1972), the record label fulfils the position of cultural gatekeeper in this creative industry. This implies that they are tasked with the selection of the musical outputs that deserve coverage, by choosing those musicians and bands that are of high enough quality to be heard by a large audience. Moreover, main topics that seem to be emphasized in music business literature concern intellectual

6 property rights challenges, and the effects of disruptive innovations on consumer behaviour.

Whilst little attention has been given to the strategic aspect concerning the intermediaries. This subject is very valuable to study since the competitive field consists of a great variety of players, for whom the stakes are high.

In the past years, many changes have had a great effect on the overall music industry, mainly due to technological innovations (Alexander, 1994b; Arditi, 2017a). There has been a transformation from an ownership model, with physical and digital formats, to an access model, in which the consumer has had to adapt to different manners of acquiring and listening to music

(Wikström, 2012; Kolar, 2017). An accompanying trend is one of disintermediation and empowerment, which has changed the dynamics of the industry greatly (Fox, 2004; Hracs,

2012; Musgrave, 2017). It was expected that these developments would render record labels obsolete. However, the opposite was proven to be true, as the record label is still a very important entity in the industry (Moore, 2012). To emphasize the prominence of the record companies, it has been established that they are the primary financers in the music industry, spending large sums of money on artist and repertoire, and the marketing of records (WIN,

2017). A total revenue of $15.7 billion was generated in the global recorded music industry in

2016 (IFPI, 2017). More specifically, one of the largest record labels worldwide, Universal

International Music, reported a revenue of $5.3 billion in 2016 (Schneider, 2017). This shows the size and importance of the industry and the promising returns that are being attained by successful labels.

All these factors result in an industry that is highly dynamic and very interesting to study. This thesis intends to shed more light on the competitive environment of the recording industry, with a focus on the emergence of new record labels. The strategic move of new entry is of great importance to the new entrants as well as the incumbents since it affects the levels of concentration in the industry. The complex structures of large labels with many different

7 subsidiaries and business units, the distinction between the types of labels, and strategic alliances that can be formed between them make for an industry that is complicated but exciting to study. The purpose of this study is to answer the research question that is formulated as follows: “Can the level of strategic integration and the structure of a company explain the likelihood of entry and performance of new record labels in the music industry?” This will be researched by thoroughly analysing music metadata on record labels, which is stored in databases that are accessible to the public.

The remainder of this thesis is structured as follows. First, the literature review will present the basis for the strategic perspective, and explain why new entry is a phenomenon that is crucial for an industry. Then, the music industry value chain will be described, and the strategic perspective will be applied to this industry by looking at diversification, integration, and types of record labels. The literature review provides the theoretical basis for the hypotheses that will be formulated subsequently. Additionally, the research method will be explained in detail and the approach taken for the analysis of the data will be clarified. This will allow the results to be presented and discussed in order to evaluate the findings that were discovered. This thesis will be ended with a conclusion, which includes the implications, the limitations, and the options for future research.

8 2. Literature Review 2.1 Strategy and New Entry Companies in an industry can have different objectives they want to achieve. How they address these priorities within their corporations can vary. These differences in approach with regard to attaining their principal goals are referred to as the strategy of a firm. There has been written a lot about strategy in business and management literature in the past, and it has become a dominant subject when explaining firm survival and the configuration of the competitive environment. A strategy is defined as a coordinated set of actions designed to exploit core competencies to achieve competitive advantage (Hitt, Ireland & Hoskisson, 2012). In a strategic plan, businesses make decisions on how they want to pursue their corporate goals.

Business-level strategies describe how firms aim to fulfil objectives by exploiting their core competencies to attain competitive advantages. These strategies focus mainly on specific product markets (Jones & Butler, 1988). Whereas corporate-level strategies are consciously formulated with a long-term focus that aims to specify the management of several different businesses (Pehrsson, 2006).

One of the most prevailing models in strategic management literature is the Five Forces framework by Porter (1979). In this framework, there are five competitive forces that are argued to structure competition in an industry. Porter (1979) reasons that awareness of these forces can help companies to formulate a comprehensive strategic business plan. One of these

Five Forces is the threat of new entrants. This force refers to the threat that new competitors pose to the incumbents in an industry. When there is an influx of new entrants, the ability of the incumbents to achieve profitable returns could be affected. This threat can be reduced by increased barriers to entry or by the expected retaliation of the incumbents. These factors can act as a deterrent for the possible new entrants (Porter, 1979). Although there are many motivations and reasons to start a new venture, entrepreneurs must be aware of these barriers

9 to entry (Birley & Westhead, 1994). Barriers to entry are defined as the market conditions that raise the cost of entering the market for new firms so that incumbents can earn profits in the long run (Bain, 1956). Porter (1979) argues that there are six sources that can heighten the barriers: economies of scale, product differentiation, capital requirements, switching costs, access to distribution channels, and government policy. Depending on the industry and its characteristics, these sources can prevent new entry from being successful. Furthermore,

Lambkin and Day (1989) argue that the development of a market is strongly related to the number and the types of companies that are entering an industry. However, not all incumbents are affected similarly when a new firm enters the market. This is caused by the forces that accompany new entry, and how these influence firms either in a competitive or a cooperative manner. A new entrant can, for example, intensify rivalry in an industry where heavy competition is already waging. Whereas, new firms can also help to legitimize markets, leading to an increase in demand from consumers (Özsomer & Cavusgil, 1999).

An industry is labelled highly concentrated when most of the total market share is controlled by only a few players. High barriers to entry prevent the entrance of new actors into the market, hence causing increased concentration levels (Tremblay & Tremblay, 2012). The notion of a connection between concentration and new entry is underlined by the theories supporting the organizational ecology perspective on firm survival. More specifically, by the theory of resource partitioning and its proposed effects on industry configurations (Baum,

1996). In this theory, a distinction between generalists and specialists is made. Generalists are the large players that dominate a field and can operate in multiple types of environments.

Whereas the specialists focus on certain peripheral (i.e. niche) areas as they have increased chances of survival when they can target narrow and homogeneous segments (Carroll, Dobrev

& Swaminathan, 2002). Carroll (1985) researched the newspaper industry where generalists and specialists inhabit the market. This industry is known for its increased barriers to entry due

10 to economies of scale, leading to an environment with high levels of concentration. Economies of scale imply that large companies can produce their products at a lower price because the marginal cost of producing a unit declines as the produced quantity increases (Hitt et al., 2012).

These conditions combined with consumer tastes that are heterogeneous are the assumptions for the theory of resource partitioning. This theory reasons that rivalry among generalists in the main areas of the industry leads to the opportunity for specialists to occupy the peripheral areas in the same industry without having to face generalists in direct competition (Reis, Negro,

Sorenson, Perretti & Lomi, 2012). This is because the generalists focus on the mainstream markets so that they can reach a large group of potential customers, whereas the specialists focus on those domains that are more distinctive, and allow for a more targeted approach

(Carroll, 1985). This theory has received wide attention and allows for application to many industries. It is related to the subject of strategic new entry because it provides an explanation as to why it is still possible and attractive for new companies to enter a market, even though the concentration levels are high.

2.2 Strategic Integration

A newly entering company is not always a completely new venture initiated by a layman. The origins of a new entrant can be diverse. If the company has already participated in the same supply or value chain, then these expansion efforts are termed horizontal or vertical integration (Caves & Porter, 1977). These actions are both undertaken to acquire more market power (Hitt et al., 2012). Horizontal integration happens when a firm acquires or starts a competing firm in the same part of the supply or value chain. This could lead to several players becoming very powerful because of the increased levels of concentration in an industry

(Tarzijan, 2004). Mergers and acquisitions are expansion strategies that are pursued by many companies looking to spread their presence in an industry. Horizontal mergers and acquisitions

11 are not only undertaken to increase market power but are also used to reorganize resources or to achieve scale efficiencies. This could make organizations more efficient, and often also more profitable because of the reduced overhead costs (Capron, Dussauge & Mitchell, 1998). On the contrary, vertical integration entails a firm moving up or down the supply or value chain.

Forward vertical integration involves a firm integrating further along the supply chain.

Whereas backward vertical integration involves a firm moving towards the start of the supply chain (Harrigan, 1985).

More specifically, Mol, Wijnberg, and Chiu (2012) distinguished three modes of entry that can be pursued when entering an industry. The first one is called ‘new entry de ipso’, this means that the entrant has previous experience in the same industry and in the same part of the value or supply chain, and has therefore acquired useful knowledge and connections that make new entry significantly easier. This is similar to the notion of horizontal integration. The second entry mode is called ‘new entry de intra’, this refers to the actors, who are already part of the same value or supply chain, but have gained experience in a different task. This is similar to the notion of vertical integration. The third one is called ‘new entry de novo’, which refers to entrants who are completely new to the industry and have no experience whatsoever.

Furthermore, there are several other studies that mention an additional mode of entry, that is referred to as ‘new entry de alio’. This mode entails the entry of a firm, that already has business experience in a similar market but is looking to diversify their production (Carroll, Bigelow,

Seidal & Tsai, 1996; Giarratana, 2008; York & Lenox, 2014). These incumbents already have the resources and capabilities that should make diversification notably easier.

2.3 The Music Industry

In general, the music industry is referred to as a value chain rather than a supply chain.

This is because distinct value is added to the product in every step of the process along the

12 Figure 1. Music Industry Value Chain (Bockstedt et al., 2006)

different stages (Mol, Wijnberg & Carroll, 2005). The basic value system of the music industry is generally believed to consist of several stages. This is illustrated in figure 1, which depicts the value-adding entity and the main task for which the entity is responsible. It starts with the composition, recording, and production of music by artists and musicians. Then it moves along to the record labels that sign the artists they believe have potential for success. This entails the artists to give their musical recordings to the label. The label then coordinates the licensing, marketing, and promotion of the recordings (Graham, Burnes, Lewis & Langer, 2004).

Normally, it is also the case that they arrange the manufacturing and distribution of the physical or digital end-product with other actors. The distributor then makes sure the product ends up at the correct retailers, from which the consumer can purchase the product (Bockstedt et al.,

2006). The terms record company, record label, and music label are used interchangeably in the literature and this thesis. They are often referred to as the cultural intermediaries of the music industry. The term cultural intermediary was introduced by Bourdieu (1984). The main emphasis of this terminology lies in the fact that the individuals and firms that are termed cultural intermediaries are in the spots between the creative input and the commercial output.

They are the middlemen between the production and consumption (Negus, 2002).

One of the main topics in music business literature in the past few years is the influence of technological innovations (Alexander, 1994a; Graham et al., 2004; Arditi, 2014a). For this study, these developments are of importance because of the impact they have had on the industry concentration. New technologies often are positive for newcomers since they are likely to reduce the barriers to entry (Hesmondhalgh, 1998; Alexander, 1994b). Three main parts of the industry have been disrupted or heavily influenced by these new technologies.

13 Firstly, there have been significant changes in how consumers enjoy musical recordings. There has been a transformation from a product-based to a service-based industry

(Leurdijk, Slot & Nieuwenhuis, 2012). Among consumers, there has been a clear shift from individuals who owned physical items, such as vinyl and CDs, and digital items, such as MP3s, to solely access to music via streaming or subscription services (Kolar, 2017; Arditi, 2017a).

Another trend that is connected to the changing consumer patterns, is the unbundling of music.

Consumers now have the opportunity to ‘cherry pick’ the specific songs they want to purchase instead of having to buy complete albums (Ferraris, Auberty & Rao, 2008; Elberse, 2010).

Secondly, there are increasingly more opportunities for artists to reach their audiences due to the possibilities presented by new media channels. It is argued that this could lead to the disintermediation of the value chain and increased empowerment for musicians (Leenders,

Farrell, Zwaan & ter Bogt, 2015). Digital equipment for recording, mixing, and mastering is becoming more accessible for artists since the costs of these tools have been decreasing. This in combination with the introduction of MySpace, YouTube, and Soundcloud, should have shifted the balance of power in favour of the artists (Homan & Gibson, 2007). However, many artists are still struggling to make a living as professional musicians (Hesmondhalgh, 2013).

Leenders et al. (2015) concluded that even though the industry has undergone major transformations, record labels remain an important part of the value chain because of the associations they hold with global media. This notion is supported by Mol et al. (2012), who found that connections to mass media gatekeepers are vital to the success of music industry actors.

And finally, the industry is influenced by the effect of the long tail. This theory argues that because of the digitalization of content, there is an increase in the number of recordings being released (Anderson, 2006). This is made possible by the advent of the Internet and other innovations that have made the production and distribution of musical recordings less costly,

14 and thus more accessible. This notion is supported by research done by Bourreau, Gensollen,

Moreau, and Waelbroeck (2012), who concluded that digital record labels that only release recordings through the Internet, on average produce more music than a regular record label.

This suggests that two sources that normally heighten barriers to entry as argued by Porter

(1979) have decreased, namely the capital requirements needed and the access to distribution channels. Thereby making new entry into the industry easier and more attractive.

2.4 Majors vs. Indies

The record label stage of the music industry value chain can be categorized as oligopolistic, meaning that there are several very strong firms that dominate the market

(Burnett, 1992; Homan & Gibson, 2007). Currently, there are three major labels, namely

Warner Music Group, Sony Music Entertainment, and Universal Music Group. A record label qualifies as a major, per the Association of Independent Music, when it has more than 5% of the world market’s sales of records. These record labels can provide an artist with additional services, as they often have their own distribution channels (Lindvall, 2012). This is because typically they are a part of a music group, that is owned by an even bigger conglomerate holding. These holdings have several divisions that can even be focused on non-music ventures

(Rutter, 2016). Major labels are structured in a multidivisional manner, implying that the main company has many subsidiaries, frequently referred to as sublabels or semi-independent labels

(Lacey, 2014; Dowd, 2004b). In appendix 1, there is a partial overview of the multidivisional structure of the three major record labels to illustrate the expansiveness and complexity of these companies. The development of these multidivisional structures took many years and consisted largely of starting, acquiring, and merging various sublabels (Scott, 1999; Shuker, 2017). This structure is related to the corporate-level strategy of related linked diversification that is often pursued in the music industry. This strategy proposes that there is a large parent company with

15 multiple sublabels, which share some activities, core competencies, and resources (e.g. distribution) (Tanriverdi & Lee, 2008). It was proven by Palepu (1985) that the performance of firms with a related diversification strategy is significantly better than the performance of firms with an unrelated strategy, which infers that no activities are shared among subsidiaries.

Majors are often contrasted against independent labels (indies). The main difference between the majors and the independents is that the major labels have an extremely large market share, and the opportunity to make use of their integrated distribution channels. The term ‘independent’ might be somewhat inaccurate, as these labels often do depend on one of the three major labels for the distribution of their music (Scott, 1999; Passman, 2015). For example, the Warner Music Group has a subsidiary called ‘Alternative Distribution Alliance’ that is responsible for the distribution of records for more than 100 independent labels (ADA,

2018). This is often done because a distribution network is extremely tough and costly to set up independently. It is often a lot simpler to contract another company to take over these tasks

(Alexander, 1994b). Indies are regularly categorized as small niche labels, but this is not always true because independent record labels can also be large organizations with their own sublabels.

They are generally known for their artist-friendly deals, and their ability to spot new trends and promising artists (McLeod, 2005). Moreover, the independent labels represented a large share

(38.4%) of the recorded music market in 2016. This percentage has been steadily increasing over the past few years, with a relative growth of 0.9% from 2015 to 2016 (WIN, 2017).

Hesmondhalgh (1996) reasons that major and independent record labels can have several types of relationships. The first type is international licensing, where smaller labels negotiate a deal with a large label concerning the licensing of recordings in various countries.

The second type is the distribution deal, where close cooperation between the small and the large label is required. The larger label will then take care of the distribution of records for the smaller labels. At times, this also involves a funding deal, where the small labels sell some of

16 their stakes to the larger label. The third type of relationship is the acquisition. This is a type of relationship that the majors have been pursuing extensively during the past 20 years, as a part of their expansion strategy. The licensing and distribution relationships are often seen as the setting stages for the oftentimes inevitable acquisition. Acquisition by a major is also an end-stage that is denoted by Wallis and Malm (1984) in their framework that depicts the possible routes of development for small record labels (see appendix 2).

Highlighting the distinction between majors and indies is essential as both types have their own advantages and disadvantages. The majors have a lot of power, resources, and connections that could prove valuable, whereas the indies have a better reputation regarding artist deals. The type of record label is of importance when looking at strategy because it has been proven that company structure affects performance negatively when a company’s strategy is not complemented with a corresponding structure (Rugman & Verbeke, 2008).

2.5 New Entry in the Music Industry

The modes of entry that were stated before, can be applied to new record labels entering the music industry (Mol et al., 2012). Firstly, the de ipso entrants are the new subsidiaries of large labels that opt to integrate horizontally by starting or acquiring new sublabels. This entry mode can be pursued by a major label or an established independent label. Secondly, the de intra entrants were identified as the entrants who have been part of the same value chain but have accumulated experience in a different task. This could be the case when artists, distributors, or manufacturers attempt to integrate vertically by starting their own record label.

And thirdly, there are de novo entrants who have no previous experience in the music industry, hence they will start with no prior knowledge or connections.

Because of increased reliance on technology and the prominence of the Internet in our daily lives, the music industry value chain has undergone some transformations. Core tasks of

17 record labels such as the marketing of albums, now mainly take place on the Internet through social media channels (Leurdijk et al., 2012). The manufacturing stage of the value chain can be skipped completely because a wide audience can be reached with an end-product in a digital format (Wikström, 2012; Vaccaro & Cohn, 2004). These developments have lowered the barriers to entry and therefore have made it more attractive for individuals to start their own record labels (de novo entry). This is underlined by the fact that the Association of Independent

Music has uploaded a booklet with guidelines for starting your own record label to their website

(AIM, 2016). Moreover, due to the phenomenon known as value chain envy, entry into this stage of the music industry value chain is most attractive to prospective new entrants (Mol et al., 2005). This is because the ratio between value capture and value creation is most uneven in record label stage, hence creating more favourable prospects of attaining increased returns.

Individuals that have gathered knowledge about this unequal division could experience a feeling of envy. This feeling could provide motivation to enter this exact value chain stage.

Starting a company requires a lot of effort because a company needs to be registered legally, and other organizational tasks need to be fulfilled (e.g. choosing a name, hiring personnel, setting up a website) (McKay, 2009; Reis et al., 2012). The larger labels have a lot of experience in the industry because many were established years ago. This suggests that they are aware that in order to expand their business, the acquisition of a label is a less intensive approach than starting a new label from scratch. Therefore, it could be argued that it is the strategic intention of larger labels to acquire other labels after a phase in which they had time to establish themselves. This would indicate that a strategy of horizontal integration through acquisition would be preferred over horizontal integration through starting a new subsidiary by existing major and independent labels. I expect that because of the technological advancements and the disintermediation of the value chain, more labels enter the industry without any previous experience (de novo entry) and that established labels prefer to acquire rather than

18 start new subsidiaries due to the effort needed, which allows the following hypothesis to be formulated:

H1a. A de novo entrant is more likely to be entering the industry than a new subsidiary

initiated by either a major or a large independent label (de ipso entrants).

A completely new record label (de novo entrant) will often focus on a niche to avoid direct competition with industry heavy-weights. This is in accordance with the theory of resource partitioning, which argues that specialists are more likely to focus on narrow and homogeneous segments, whereas generalists often concentrate on large heterogeneous segments (Carroll et al., 2002). The focus on a certain niche may entail exploring peripheral genres, which often leads to more creative and experimental releases (Negus, 2002). It was argued that these small new labels are more adept at finding talent in fringe genres than the large labels (Burnett, 1996; Hesmondhalgh, 1996). This is mainly because the structure of large labels is too complex, whereas the structure of the new label is likely to be flat, which makes communication and decision-making easier (Carzo & Yanouzas, 1969). This notion is also supported by the theory of the long tail, which reasons that targeting niche markets is more fruitful because these are often ignored by the commercially focused majors (Anderson, 2006).

However, it is very hard to achieve good results because of the inherent uncertainty and continuous oversupply in the music industry (Peltoniemi, 2015). Attaining returns that are sufficient might be especially hard for the de novo entrants that focus on peripheral genres that are not well-known to the public.

These harsh circumstances are also encountered by the subsidiaries of established labels. Still, these de ipso entrants have parent companies that could supply them (if necessary) with resources, expertise, and knowledge through the related linked diversification strategy

(Montgomery & Singh, 1984; Mol et al., 2012). Additionally, these companies can benefit from economies of scale, which allows them to deliver more output to the market at lower costs

19 which increases the margins. This is possible because of the large production size and the incremental efficiency improvements achieved through experience (Scott, 1999; Hitt et al.,

2012). This enables the de ipso entrants to establish a preferential position, which will increase their chances of success, thereby improving their performance. Hence, I expect that de novo entrants will have a harder time when it comes to performing sufficiently in comparison to both types of de ipso entrants, which brings the following hypothesis:

H1b. A de novo entrant is less likely to perform well than a new subsidiary initiated by

either a major or a large independent label (de ipso entrants).

In a similar vein, a distinction can be made between the two types of de ipso entrants, the actual majors, that are a part of a large international media group, and the independent labels that are established names but are not nearly as powerful as the majors. The distinction in market power is made based on the size of the majors, the fact that they have integrated distribution systems, and more sophisticated facilities (Musgrave, 2017; Hughes & Lang,

2003). As was mentioned before, these established record labels can pursue horizontal integration by acquiring existing companies or by starting new subsidiaries. Horizontal integration through starting a new sublabel is attractive because resources and personnel can be redeployed from another label to focus on the new subsidiary (Bradley, Aldrich, Shepherd

& Wiklund, 2011). This is in accordance with the concept of a related linked diversification strategy, in which core competencies and resources are shared or provided by the parent company (Tanriverdi & Lee, 2008). This approach will most likely be less costly than the acquisition of an existing company since this can be very expensive because a premium is often required to be paid on top of the fair value of the business’ assets (Li, 2011). This might not be an issue for the majors because these large corporations are expected to have more excess resources (Ingham, 2017a). To illustrate, Spinnin’ Records, a Dutch independent record label started in 1999, was acquired by Warner Music in 2017 for an amount that was estimated to be

20 around $100 million (Ingham, 2017b). These large amounts of money would be impossible to secure for most independent labels. Thus, this would suggest that a large independent label would pursue a strategy of horizontal integration through starting sublabels more often than a major label, causing the following hypothesis to be formulated:

H1c. A new subsidiary initiated by an established independent record label is more

likely to be entering the industry than a new subsidiary initiated by a major label.

An additional distinction between these two types of de ipso entrants is that independents are known to be more engaged and care more about musicians and bands

(Hesmondhalgh, 2013). Therefore, it is more likely that these labels would engage in starting new subsidiaries that could focus on promising new genres or niches so that they can provide the best service for their artists. This follows the subsidiary’s strategy type of a specialized contributor that was first identified by White and Poynter (1984). This implies that considerable expertise in specific functions is obtained in these subsidiaries by focusing on certain markets or products. Whilst a tight connection with the parent company is maintained (Birkinshaw &

Morrison, 1995). These expert subsidiaries are also proposed by the theory of resource partitioning which argues that specialists often opt to focus on niches (Carroll et al., 2002). I expect that these expansion endeavours can be pursued successfully because of this deliberate specialization, since it was proposed that specialist firms often outperform generalists, because of the explicitly developed human capital in these firms (Gompers, Kovner, Lerner &

Scharfstein, 2008). For a specialized record label, this could lead to attracting more niche artists and offering them expert knowledge and resources, which should allow for a performance that is above average. This would suggest that a large independent label would pursue a strategy of horizontal integration more effectively than a major, resulting in the following hypothesis:

H1d. A new subsidiary initiated by an established independent record label is more

likely to perform well than a new subsidiary initiated by a major label.

21 2.6 Diversity in the Music Industry

For a long time, increased market concentration was believed to decrease diversity and innovation in musical output. This idea was the basis of the ‘old popular music model’, in which large labels were presumed to be more interested in artists that were guaranteed to become popular and bring in a lot of revenue. The primary goal of the record label was profit maximization, which could be attained by focusing on confirming consumer tastes rather than disrupting them (Rothenbuhler & Dimmick, 1982). During the 1980s, there was a transition period, where concentration in the industry was very high, yet the diversity of output also increased significantly. One of the speculated causes of this phenomenon was the cooperation between large and small labels through licensing and distribution deals (Burnett, 1992; Lopes,

1992). Thus, the ‘old popular music model’ is not applicable anymore in the music industry

(Burnett, 1996). Nowadays, large record labels produce and release music in all kinds of genres, so that no unsated demand exist among the consumers (Lopes, 1992). It was argued by

Peterson and Berger (1975) that a more concentrated environment would lead to a more entrepreneurial mind-set for prospective firms. This would then induce the entry of new specialist organizations that could focus on certain niches, which is in line with the resource partitioning theory (Carroll, 1985). Therefore, when there are more record labels entering the industry, an increase in the diversity of the output is expected (Peterson & Berger, 1975).

There are several reasons why a firm would opt for a strategy where there is an emphasis on the diversification of output (Hitt et al., 2012). In the fundamentally uncertain music industry, the reduction of risk is one of the main concerns. Rothenbuhler and McCourt

(2004) argue that the music industry is possibly the most ambiguous and volatile of all the creative industries, as consumer tastes are extremely fluctuant and the supply of musical output is abundant. One of the diversification strategies that is frequently used to tackle this problem, is portfolio management. This strategy allows the spreading of risks across potential sources

22 of income. In effect, the diversifying record label releases several records across different genres and audiences, to ensure they will always get some returns (Hitters & van de Kamp,

2010). Genres that are well-known and are loved by many consumers, are steady income sources. Whereas other genres might be riskier and have returns that are harder to predict, so the allocation of resources to these genres should be done with care (Negus, 1999a).

Diversification strategies, such as portfolio management, are related to the aforementioned mode of entry that is known as ‘new entry de alio’, which refers to existing firms that opt to diversify their output (Carroll et al., 1996). De alio entrants already have established legitimacy, expertise and resources in their industry, which should make diversification notably easier (York & Lenox, 2014). The focus of this entry mode is on the output of a record label, which are the musical recordings that are being released. For a record label, diversification could consist of adding new genres to their repertoire of releases (Hitters

& van de Kamp, 2010; Negus, 1999a). Referring to the example that was given before, Spinnin’

Records started as an independent record label solely focusing on electronic music. However, after several years they decided to diversify their genre range, by releasing songs that are categorized as pop or hip-hop (Discogs, 2018b). This suggests that de alio entrants try to diversify their releases by expanding into multiple genres to hedge against risk and uncertainty.

This entry strategy can be undertaken by all types of record labels that were covered in the previous section. However, de novo entrants have to build a reputation for themselves in order to be able to attract and sign more artists. Petkova, Rindova, and Gupta (2008) argue that new ventures, that establish favourable perceptions through direct interactions in small specialized groups, can quickly build a solid reputation. Therefore, it might be more fruitful for de novo entrants to focus on a specific genre and offer specialized care for these artists.

This also applies to the large independent labels, which are expected to maintain a specialized approach when establishing subsidiaries through horizontal integration. It was argued before

23 that independent labels are more focused on providing the best environment for their artists rather than wanting to maximize profit (Hesmondhalgh, 2013; Rudsenske & Denk, 2005).

Consequently, I expect that they will start a sublabel to focus on one particular genre, which should be more attractive to artists that release music in this genre, as they would be able to provide these artists with the best resources and expertise pertaining to this genre. Conversely, it can be assumed that major labels will start sublabels with as goal to cater to a public that is as broad as possible to maximize profits (Burnett, 1992). Therefore, it is suggested that subsidiaries of major labels are started with a strategy of risk hedging in mind. This leads to the following hypothesis, in which I expect that sublabels pursued by majors use portfolio management strategies to quickly spread their releases across multiple genres to secure more returns:

H2. Subsidiaries initiated by majors tend to diversify faster into genres different from

the genre of their first release (de alio entry), in comparison to other new entrants.

2.7 Vertical Integration in the Music Industry

Another predominant type of record label in the music industry is the vanity label. This is a record label that is initiated by an established record label and is fronted by a popular artist or band. It is suggested that this artist is running this company and is therefore taking care of the day-to-day operations. However, these labels are wholly or partially owned by the original record label of that same popular artist (Canetta & Winn, 2002). An example is Shady Records, a label founded by Eminem, on which he released his own music, as well as records from other artists (Joy, 2017). Nevertheless, this record label is a subsidiary of Universal, the parent company that also owns the label Eminem signed to originally (Aftermath Entertainment). This type of label is similar to the aforementioned subsidiaries of established record labels (de ipso entrants) with the exception that vanity labels are fronted by a well-known artist. The

24 association with a famous artist is synergetic as it both uplifts the credibility of the newly founded label, as well as the integrity of the artists (Rutter, 2016).

Large record labels often pitch the opportunity of starting a vanity label to artists to keep them satisfied and occupied (Rutter, 2016). However, this is not always the case, as some artists choose to start their own label, completely independent of any other label (Bray, 2010).

This is a form of forward vertical integration (de intra entry), as the artist moves forward along the value chain. They do not only deliver music but also take over the tasks of the record label

(Harrigan, 1984). This is in line with the trend of disintermediation which was fuelled by the advancements in technology (Hesmondhalgh, 1998). An advantage that the de intra entrants might have over de novo entrants, is that they have been part of the music industry value chain for some time. Therefore, they may have been able to establish relationships with industry actors, which could make their entry easier and more successful (Hughes & Lang, 2003).

Accordingly, a distinction between the two types of artist-fronted labels can be made.

Vanity labels that are initiated by established labels (de ipso entry) are posed against labels that are initiated by artists independently (de intra entry). A lot of negative publicity has been surrounding labels in the past years concerning record deals that rip off artists and contracts that include harsh conditions (i.e. ‘slave deals’) (Kaye, 2015; Messitte, 2015). The collective reputation of record labels has suffered as a result of this. Therefore, artists might be less willing to sign a deal that they deem to be exploitative when they can start a label themselves.

Referring to Wallis and Malm (1984), all three conditions needed to start a new record label are present for these artists, namely dissatisfaction with the establishment, the acquisition of know-how, and accessibility to equipment. Additionally, similar to de novo entrants, artists might also experience value chain envy (Mol et al., 2005). For artists, this could have an extra dimension as they might pursue vertical integration to be able to retain more of their royalty payments. Hence, I anticipate that when an artist-fronted label is started, it is more likely to be

25 initiated by artists themselves without any help from an existing label, which leads to the following hypothesis:

H3a. Record labels initiated by artists independently (de intra entry) are more likely to

be entering the industry than vanity labels (de ipso entry).

However, it is not expected that these de intra entrants will be able to establish the level of performance that is attained by vanity labels. The same reasoning applies as for hypothesis

1 since the vanity label has a parent label that could provide them with back-up whereas the de intra entrant does not have access to those resources. Therefore, when artists start their own label to release their output, I expect them to perform worse than a vanity label.

H3b. Record labels initiated by artists independently (de intra entry) are less likely to

perform well than vanity labels (de ipso entry).

Table 1. Overview of hypotheses

H1a A de novo entrant is more likely to be entering the industry than a new subsidiary initiated by either a major or a large independent label (de ipso entrants). H1b A de novo entrant is less likely to perform well than a new subsidiary initiated by either a major or a large independent label (de ipso entrants). H1c A new subsidiary initiated by an established independent record label is more likely to be entering the industry than a new subsidiary initiated by a major label. H1d A new subsidiary initiated by an established independent record label is more likely to perform well than a new subsidiary initiated by a major label. H2 Subsidiaries initiated by majors tend to diversify faster into genres different from the genre of their first release (de alio entry), in comparison to other new entrants. H3a Record labels initiated by artists independently (de intra entry) are more likely to be entering the industry than vanity labels (de ipso entry). H3b Record labels initiated by artists independently (de intra entry) are less likely to perform well than vanity labels (de ipso entry).

26 3. Data and Methods 3.1 MusicBrainz Database For this research, I opted to mainly work with the MusicBrainz database, since this database stores the most complete information on record labels (MusicBrainz, 2018a). The entity label is of importance for this study, and contains several keys that will be used as variables (see appendix 3 for an overview). This database is driven by music metadata that is generated through music information retrieval research (Futrelle & Downie, 2002).

MusicBrainz is an online encyclopaedia that aims to collect metadata for any kind of musical output, based on contributions from the public (Porter, Bogdanov & Serra, 2016). It is a community-based (open-source) database, that is regarded to be of high-quality, thereby collecting information that is useful for various types of research (Vigliensoni, Burgoyne &

Fujinaga, 2013; Stutzbach, 2011). All the core data in the database is available to individuals under the project’s Creative Commons license and is therefore allowed to be used for the desired purpose of this study (Hemerly, 2011; Creative Commons, 2018). However, using a database that is community-based and relies on amateur contributions could pose some limitations. It has been researched whether this could harm the reliability and validity of the data. On several bases, it was concluded that the quality of the data was not impaired by the fact that it was contributed by non-professionals, and is therefore suitable for academic research purposes (Marshall & Shipman, 2003; Zhou & Davis, 2005).

3.2 Sample

A copy of the MusicBrainz database was obtained from which a new file was compiled that contained all the information on record labels based on the record label ID. Because a lot of the data for the exact month and day of entry and exit were missing and were not important for further research, these variables were removed. The total dataset consisted of over 140,000

27 entries for record labels, dating back to the early 1800s. Firstly, the most crucial event in this research is the entry of a new record label, which is denoted by the first year that a label releases a recording, as from this moment the firm is an operating entity. The database was sorted on entries which had a value for entry year, the others were discarded. For several entries in the sample, this value was changed to the year of their actual first release, as in some cases these values were not congruent. Secondly, I filtered out all the label types that did not focus on producing new music, as I only want to research the record labels that release original and new music. Thus, the label types that were included in the sample are original production, production and imprints. Thirdly, the choice was made to focus on new entrants from the beginning of 2005 until the end of 2017, since the dynamics of the industry have changed significantly after the introduction of many innovative technologies. Burkeman (2009) argues that since 2005 most individuals use the Internet on a daily basis, and are able to use it for their desired purpose (e.g. setting up a MySpace or YouTube account). Also, Leenders et al. (2015) use 2005 as starting year for their research in which they measure new media adoption among artists. Then, I opted to focus on one country to control for the fact that the circumstances for starting a company might be different in various countries, thus increasing the internal validity of the design. The most appropriate country to use for the sample is the United States because per the IFPI (2017) the United States is the world’s largest recorded music market. After these restrictions, the dataset consists of 457 cases, which is sufficient and feasible for my research.

Once the necessary data was collected, it was concluded that 31 entries in the dataset did have an entry year but had not released any music. These entries were discarded, which led to a total sample of 426 record labels. The main limitation of this research design is a phenomenon referred to as selection bias. This means that the selected sample is biased because only labels that actually entered the industry and are mentioned in the database are included in the final sample. This issue will be addressed extensively in the section on limitations.

28 3.3 Variables 3.3.1 Hypothesis 1 DV. In order to make suggestions about the performance of the record labels after new entry, additional variables need to be compiled. Mauboussin (2012) argues that a good measure of performance is a metric that focuses on the corporate goals a firm wants to attain. The main corporate objective of a firm in an oligopolistic industry is long-term survival, according to

Rothschild (1947). Long-term survival could be attained by frequently releasing new output, and thus generating continuous returns. This is because when a released song or album has had disappointing returns, it is less likely that the label will produce more output. There might not even be sufficient resources to do so, as the break-even point for the previous release has not been reached (Rothenbuhler & McCourt, 2004). Hence, I deem the number of releases produced by a label after entry, an appropriate metric to measure performance. The variable that was added, contains the total number of releases by each label after their entry until the end of 2017. This data was retrieved from a different dataset that was also acquired through the MusicBrainz database. The record label identifier was used to connect the two datasets, after which the frequency of releases was used to compile the new variable. To correct for the fact that labels entered the industry at different times, an additional variable was created which calculated the average number of releases per year after entry into the industry. This was done by dividing the value for the number of total releases by the difference in years between 2018

(or exit year) and the entry year of the record label.

IV. A framework will be used to categorize the types of labels in the database. This classification was established by using the MusicBrainz URL and the other label identifiers to find further information on the origins of the labels. The main information sources were

MusicBrainz and RateYourMusic, which provided the name of the parent company (if applicable), from which could be established whether the parent was one of the three majors or an independent label. Discogs and the official websites of the record labels were used to

29 check this information. The framework consists of three types of record labels. Firstly, there are new subsidiaries (de ipso entry) that are initiated by one of the three major labels. Secondly, there are new subsidiaries (de ipso entry) that are initiated by large independent labels. Thirdly, if there is a new entrant that is being established wholly independently, it is classified as a de novo entrant. For H1c and H1d, a dummy variable was created that only contained the de ipso entrants.

3.3.2 Hypothesis 2

DV. For the second hypothesis, complementary information and variables were needed.

The new dependent variable is the time taken until the first diversification in genres by a record label. For every record label, the genre of the first release was collected, after which it was researched if there was a second genre that was pursued, and if so in which year this had happened. Discogs proved very helpful in attaining this information. Finding the correct values was done by looking up the record label, then sorting the releases on date, and checking which genre belonged to the first release. Then, the same sorting system was used to figure out whether a second genre was pursued, and in which year this was done. The values for three new variables (first genre, second genre, and year of second genre) were added to the dataset.

With these values, the dependent variable (time to diversification) could be calculated by subtracting the entry year from the year that the second genre was pursued.

IV. The same classification scheme that was used for hypothesis 1, can be applied to this hypothesis. The distinction between de ipso and de novo entrants is discarded. The only difference made is between subsidiaries of major labels and independent labels, which could be either subsidiaries of established labels or completely new ones.

30 3.3.3 Hypothesis 3

DV. The measure for the performance after entry is the average number of releases until the end of 2017 (or exit). This is the same dependent variable that was used for hypothesis 1.

IV. For the third hypothesis, additional information was needed on the origins of the record labels. This information was largely obtained from RateYourMusic, as this website provides information about the founder(s) of record labels. Any label that was founded by individuals that already had experience in the music industry value chain (e.g. producer, musician, band), was categorized as a de intra entrant. Then it was checked whether the label was associated with an established record label, if this was the case, the label was categorized as a de ipso entrant. A dummy variable was created for the two different types of artist-fronted labels, consisting of a label that is initiated by an artist independently or a vanity label that is initiated by an established record label.

31 4. Results 4.1 Hypothesis 1 For all tests, a statistical significance level of α= 0.05 is used. A detailed description of all test results can be found in appendix 8.4. Several tests were used to check for normality, which established that the data for the dependent variable that measures performance was not normally distributed (Ghasemi & Zahediasl, 2012). However, since the sample size is sufficiently large, there should not be any problem as parametric tests perform well even with skewed distributions (Frost, 2015). However, to confirm that these tests provide the correct outcome, complementary non-parametric tests are performed. From the sample of 426 new labels, it was found that 9.4% are sublabels of a major, 13.4% are sublabels of an established independent label, and 77.2% are completely new independent labels.

For H1a, a chi-squared goodness of fit test is performed to see if the expected frequencies differ from the observed frequencies (Keller, 2012). For this test, it is expected that the three types of record labels that are present in the sample have equal frequencies. The outcome of this analysis is significant (χ2(2)=370.41, p=0.000). This leads to the rejection of the assumption that the three types of record labels enter the industry with equal likelihood. An additional binomial test is performed, which verifies that de novo entrants are more likely to enter the industry than de ipso entrants, thereby confirming hypothesis 1a. Table 2. Descriptives H1b

Furthermore, to determine which of these label types performs best after entering the industry, the number of average releases per year will be analysed. Table 2 displays the descriptives of the three groups that will be analysed. A one-way analysis of variance is performed, allowing the comparison of three groups with an interval dependent variable. The results in table 3 suggest that the there is a significant difference in performance between the

32 Table 3. Results one- way ANOVA

three groups. However, Levene’s test is not passed, meaning that the assumption of homogeneity of variances is violated, which implies that the variances are not equal. This requires additional analyses to be performed. Firstly, a Welch ANOVA is performed as a robust test of the equality of means (Moder, 2007). The results of this test confirm the outcome that was attained by the one-way ANOVA (see appendix 8.4.3). Secondly, a Games-Howell post hoc test is used to analyse the differences between the groups. This specific test was selected because of the violated assumption of homogeneity of variances (Laerd, 2018d). From this test, it can be assumed that there is a significant difference in the performance of de novo entrants in comparison to both types of de ipso entrants. Since the dependent variable is not normally distributed, I will use a non-parametric Kruskal-Wallis test to confirm these results. All conditions for this test are met (i.e. non-normal interval variable and independence of observations) (Laerd, 2018b). The test shows that there is a statistically significant difference in performance between the types of labels (H(2)=46.60, p=0.000). This means that hypothesis

1b is accepted, which leads to the conclusion that the performance of de novo entrants is worse than the performance of the other entrants based on the average number of releases.

For H1c and H1d a subset of the sample is used since there is a focus on the different

types of subsidiaries (ntot=97, nsm=40, nsi=57). Firstly, a binomial test is performed to check whether it can be assumed that these two types of record labels have an equal presence in the subset of the sample (see appendix 8.4.4). For this test, a proportion of 0.50 was established, leading to a two-tailed test (Field, 2009). The outcome of this test is not statistically significant

(p=0.104). This allows for the conclusion that there is no difference in size between the two groups. This means H1c is rejected, as both types of de ipso entrants are equally likely to enter.

33 To be able to conclude if there is a difference in the performance of these types of firms, as was predicted in H1d, an independent samples t-test is performed, for which all the conditions are met (Keller, 2012). Levene’s test is passed meaning that the variances can be considered equal. From the results of the t-test, it can be concluded that there is no significant difference in performance between sublabels (t(95)=0.265, p=0.792). Again, a non-parametric test is performed to confirm these results. A Mann-Whitney test indicates that the average number of releases after entry was not significantly different for subsidiaries from majors or subsidiaries from established independent labels (U=909, p=0.090) (Laerd, 2018c). This means that hypothesis 1d is rejected, as it cannot be inferred from the results of the statistical analyses that there is a significant difference in the performance between the two types of record labels that entered the industry horizontally.

4.1.1 Additional Analysis: Long-term and Short-term

To improve the understanding of the dynamics that may have played a role in the likelihood of new entry and the possible success of new record labels, several additional analyses are performed. Firstly, Rothschild (1947) proposed that the main objective of firms in an oligopolistic environment is long-term firm survival. However, in an environment that is as competitive and uncertain as the music industry, long-term survival might be an overly optimistic goal. It might be the case that new record labels aim at goals that are more easily attainable, such as surviving their first years in business. Hence, I will also look at the number of short-term and long-term releases per label, to see if there are significant differences between these timespans. For the short-term period, the number of releases in the first 3 years of a label’s existence will be added up. For the long-term period, the number of releases in the 4th year up until the 6th year will be added up. The average of these values will be calculated only for the record labels that were active for either more than 3 or 6 years. From the main sample of 426

34 labels, there were 392 record labels that existed for at least 3 years (ntot= 392, nsm=37, nsi=54, nni=301). Figure 2 shows that the de ipso entrants released a lot more music on average than the de novo entrants and that the subsidiaries of independent labels are slightly more productive in the first three years of their existence when compared to the subsidiaries of majors. Similar to H1b, a one-way and Welch ANOVA, and a Kruskal-Wallis test are performed respectively

(see appendix 8.4.6). All the tests show that there is a statistically significant difference in the performance of the 3 types of labels in the first three years of their existence. For the long-term timespan, 52 firms were discarded because they did not exist for at least 6 years, which led to

a subset consisting of 340 labels (ntot= 340, nsm=31, nsi=47, nni=262). In the three subsequent years, it is apparent that sublabels from majors got more productive, as can be seen in figure 2.

Whereas the sublabels from indies and the de novo entrants reduced their number of releases.

Again, a one-way and Welch ANOVA and a Kruskal-Wallis test are performed. The tests also indicate that there is a significant difference in performance between the types of labels in the long-term timespan.

Figure 2. Short-term and long-term means per label type

35 4.1.2 Additional Analysis: Electronic Music To control for the fact that some music is easier to produce and release than other types of music, I opted to rerun the analyses for hypothesis 1b with an adapted dataset. It was suggested that electronic styles of music require less effort in production than other types of recordings (e.g. classical or rock) (Colonna, Kearns & Anderson, 1993; Bourreau et al., 2012).

Hence, I expect that the output of electronic-focused labels will be larger, as the production time and cost are notably lower. Therefore, all the labels that predominantly release electronic music were excluded from the sample. To acquire this subset, I used the information gathered for hypothesis 2. Every label that released a recording that was categorized as electronic as

their first release was discarded, leading to a sample of 319 labels (ntot= 319, nsm=37, nsi=40,

nni=242). The tests for H1b (a one-way and Welch ANOVA, and a Kruskal-Wallis test) are replicated to see if there are altered results (see appendix 8.4.7). The tests show that there still is a statistically significant difference in average output between the types of labels. Allowing for the conclusion that the performance of the record labels remains approximately the same when controlled for the fact electronic music is easier to release and produce.

4.1.3 Additional Analysis: Billboard Chart Another method to measure the performance of a record label is to see whether their releases had great commercial success. This could be measured by the frequency with which recordings enter renowned hit charts (Dowd, 2004a). The Billboard Top 100 is a weekly chart that contains the top 100 most popular songs based on radio airplay, sales data, and streaming activities in the United States (Trust, 2013). Information from Billboard charts has been used frequently in past research on the music industry (Peterson & Berger, 1975; Lopes, 1992;

Burnett, 1996; De Laat, 2014). A successful performance for a record label could entail one of their releases entering this chart, essentially because exceptionally large returns could be attained when a song occupies a top rank in this chart (Rothenbuhler & McCourt, 2004). To

36 gather this information, every label in the original sample was connected to their releases through various identifiers from MusicBrainz database. Then a copy of the Billboard database was obtained, which included all the Top 100s since 1958 up until 2015 (Billboard, 2018b).

These information sources were combined to create two new variables. The first variable consists of the total number of unique songs per record label that entered the top 100. From the descriptives in table 4, it can be inferred that the subsidiaries of independent labels perform exceptionally well. Whereas, the de novo entrants have a much harder time, with only a very small percentage of songs able to reach the chart. The second variable consists of the total number of weeks for all songs per record label in the Top 100. Similar outcomes were attained for this variable, as independent de ipso entrants outperform the other entrants, as can be seen from table 4. For both variables, one-way and Welch ANOVAs and Kruskal-Wallis tests were performed (see appendix 8.4.8). These provide the same outcome as was offered in hypothesis

1b, namely that the performance of the three groups is significantly different.

Table 4. Descriptives Billboard songs and weeks

4.2 Hypothesis 2

For this analysis, the difference between a new subsidiary of a major label and a new independent label is researched. The new independent label could be either a subsidiary of an established independent (de ipso entrant) or a completely new label (de novo entrant). The statistical tests will be performed on a subset of the sample, to only include firms that have

diversified their output (ntot=190, nsm=38, nind=152). Since this hypothesis concerns a different dependent variable, two tests were used to check for normality (see appendix 8.4.1). It was established that this dependent variable is not normally distributed. Therefore, additional non- parametric tests are used to confirm the findings. To be able to determine if there is a difference

37 in how fast the types of firms diversify their output based on the genres in which they release recordings, an independent samples t-test is performed (Laerd, 2018a). From the results of the t-test, it can be concluded that there is no significant difference in the time to diversification between the sublabels of majors and the independent labels (see table 5). However, Levene’s test is not passed, meaning the assumption of homogeneity of variances is violated. The statistical output already takes this possibility into account and offers the correct test statistic considering that the variances are not equal (Field, 2013). The obtained results imply that the findings are not statistically significant (t(69)=-1.21, p=0.230). A Mann-Whitney test was performed as a non-parametric test to confirm these results. This test indicated that there was a non-significant difference in the time until diversification between two groups of record labels (U=2646, p=0.397). This leads to the rejection of hypothesis 2, which means that subsidiaries of major labels do not pursue a second genre faster than new independent labels.

Table 5. Results independent samples t-test

4.2.1 Additional Analysis: Original Grouping As these results were not as expected, the tests were rerun with the 3 groups used for hypothesis 1a and 1b (see table 6). A one-way ANOVA was performed, which showed that there is a significant difference in time to diversification when these 3 groups are posed against each other (F(2, 187)=3.212, p=0.042). These results were confirmed by a Welch ANOVA and a Kruskal-Wallis test. A Games Howell post hoc test suggests that there is a large difference in time to diversification between the two types of independent labels that were initially grouped together, which may have led to the rejection Table 6. Descriptives H2 with original grouping of the initial hypothesis.

38 4.3 Hypothesis 3

For hypothesis 3, a subset of the sample is assembled with all the cases that pertain to artist-fronted labels (see table 7). A binomial test is performed to see if there is a difference between the likelihood of entry of vanity labels and labels started independently by artists. The outcome of this test was significant, which implies that the de intra entrant, started by an artist independently, is more likely to enter the industry than a vanity label initiated by an established label, confirming H3a. Table 7. Descriptives H3

To be able to conclude if there is a difference in the performance of these two types of artist-fronted labels, an independent samples t-test is performed for which all conditions are met. From the results of the t-test, it can be concluded that there is a significant difference in the performance between the de intra entrant and de ipso entrant (t(152)=-3.613, p=0.000).

However, as can be seen in appendix 8.4.12, Levene’s test is not passed, meaning that the variances cannot be considered equal. When looking at the test statistic when no equality of variances is assumed, the results still suggest that the findings are statistically significant

(t(23)=-2.121, p=0.045). A Mann-Whitney test verifies these outcomes. These results indicate that the labels started de intra produce on average fewer releases than vanity labels started de ipso, which means that H3b is accepted.

39 5. Discussion 5.1 De Novo Entrants From the results obtained regarding hypothesis 1a, it was concluded that de novo entrants are more likely to enter than either subsidiaries of majors or subsidiaries of independent labels. This influx of new record labels is most likely facilitated by decreased barriers to entry. A possible cause for these improved circumstances for new ventures are the technological advancements that make setting up a new record company significantly easier.

This development is underlined by the additional analysis, which revealed that a large percentage (±27%) of the de novo entrants focus on electronic music as their entry genre. It is thought that it is less costly to produce and distribute music in this genre, as the production is mainly reliant on software, and the distribution is solely done through digital files over the

Internet (Arditi, 2014a; Graham et al., 2004). These developments in the music industry value chain are emphasized by the emergence of a phenomenon that is known as ‘the bedroom studio’, referring to the fact that electronic artists only need the computers in their bedroom to produce and release music (Grogan, 2011; Colonna et al., 1993). This indicates that starting a new record label has become an attainable option for everyone interested, as capital requirements have decreased, and previous experience is not a necessity.

Nonetheless, from the results of hypothesis 1b, it was determined that the performance of these de novo entrants is worse than the performance of the other entrants, based on the average output of releases. The lowered barriers to entry also imply decreased costs of competing, possibly leading to an influx of new entrants (Ferraris et al., 2008). Thereby, creating an excess of de novo entrants, which leads to heightened competition in an industry that is already known for its tough circumstances. This may reduce the chances of having a release that gets picked up by gatekeepers and attain returns that are sufficient to break-even and keep production going. Moreover, streaming services (e.g. Spotify or Apple Music) offer

40 very large amounts of content. This is because virtually every individual can upload music onto these services through companies such as CDBaby, Record Union, and TuneCore (Mortensen,

2010). These companies release your music on streaming services for a small fee (e.g. releasing a single on Spotify costs $7 per year) (Record Union, 2018). Allowing the long tail to grow longer, making it even harder to get noticed for artists in this digital world of abundance

(Anderson, 2006). To be able to attain results that allow these record labels to survive, they may need promotional campaigns to increase awareness of the existence of their products among consumers (Arditi, 2014b). This was supported by Leenders et al. (2015), who argue that large marketing budgets are a necessity for labels to stand out in the crowd, online as well as offline.

5.2 De Ipso Entrants

Hypotheses 1c and 1d are both rejected, meaning that there is no difference in the likelihood of entry and the subsequent performance of the two types of de ipso entrants. When comparing the obtained descriptives, the performance of both types of subsidiaries is very similar. A possible explanation for this could be found in the literature on mimetic isomorphism. Mimetic isomorphism is defined as the process through which organizations change over time to become more similar to other organizations in their environment

(DiMaggio & Powell, 1983). This explanation is supported by the fact that isomorphic processes are more likely to develop in environments where paths to success are not obvious, such as the music industry (Hitters & van de Kamp, 2010). Haveman (1993) argues that firms imitate those organizations in their industry that are perceived to be successful. The heightened uncertainty in the competitive environment could lead independent labels to notice how well the majors are doing (e.g. their market share of >60%) (Hitters & van de Kamp, 2010). As a result, they could try to mimic the major’s behaviour, resulting in almost equal likelihoods of

41 horizontal entry, and approximately the same level of average releases. This is exemplified by a trend that involves the expansion of large independent labels, in a manner that is similar to how the major labels established their multidivisional structures. For example, Kobalt Music

Recordings is an independent record label and a subsidiary of the Kobalt Music Group.

Moreover, the Kobalt Music Group has various subsidiaries, amongst which are companies that specialize in publishing and distributing (Kobalt, 2018). From this, it could be inferred that this independent label is trying to mimic a major label, by being part of a larger integrated music group that is structured in a multidivisional manner. This suggests that the larger independent labels researched in this study have the tendency to act like the major labels, rendering the distinction between majors and indies obsolete to some extent.

As was mentioned before, the dynamics underlying this study are inherent to the extreme uncertainty that is present in the music industry. It could be argued that this uncertainty is also intrinsic for other creative industries, such as the movie industry. In the movie industry, six large conglomerates account for a sizeable percentage of box office revenues, leading to high concentration levels, similar to the music industry (Rothenbuhler & McCourt, 2004).

Another comparable characteristic is the distinction between mainstream (major) and art house

(independent) movies (Zuckerman & Kim, 2003). Mainstream movies are often backed by complex-structured companies with large budgets. Whereas, art house movies are normally produced by smaller companies, which focus on quality (Gemser, van Oostrum & Leenders,

2007). This illustrates that similar industry dynamics apply to the movie industry, implying that the findings of this study could be replicated in this environment. However, it must be said that the barriers to entry are still relatively high, as producing a movie is still very costly

(Simonton, 2005). It is in this literature on the film industry that the term ‘mini-majors’ is introduced by Zuckerman and Kim (2003). I deem this a more appropriate terminology for the large independent labels that act like major labels, per the theories of mimetic isomorphism.

42 5.3 Internal Competition

In the additional analyses for hypothesis 1, it was studied whether short-term or long- term timespans would provide more interesting results regarding performance. This was not the case as the results of these statistical analyses provided a comparable outcome, namely that the average output levels of the three types of new entrants were significantly different.

Nonetheless, the results contained some interesting insights. For the first three years, it could be seen that subsidiaries of independent labels are most active. This could be explained by a feeling of pressure enjoyed by these subsidiaries, as they want to prove that they are worth the investment of the parent company, and therefore strive to produce a lot of output. These concerns are related to the internal competitive arena within the parent company, where there is competition amongst sister subsidiaries (Birkinshaw, Hood & Young, 2005). Moreover, the theories on typologies of subsidiary strategies argue that it is expected that subsidiaries, characterised as specialized contributors, have high productivity levels but a low return on investment (Birkinshaw & Morrison, 1995). This increased productivity could explain the higher levels of output in the first three years of existence of independent de ipso entrants. In the three subsequent years, it is observable that sublabels from majors get more active, whereas the sublabels from indies and the de novo entrants reduce their number of releases. For the subsidiaries of independents, the lower return on investment could be the cause for the reduction of releases in the long-term (Birkinshaw & Morrison, 1995). It might be the case that the parent labels have come to realize that their specialized subsidiaries have been releasing many recordings, but the returns were below expectations. Therefore, they might decide to take a step back after some years, which could explain the lower average output in the long-run.

Additionally, de novo entrants have very low output levels in the long-term, with on average less than one release per year. A similar result was found by Bourreau et al. (2012), who found that a large set (28.6%) of small record labels in their sample did not release anything in a

43 certain year. A possible explanation is that the label has not attained the success it wished for, and has given up on releasing new music without having filed for bankruptcy. Although, it could also be the case that the data in the MusicBrainz database did not have the most recent values for exit year, which may have compromised these outcomes.

Furthermore, hypothesis 2 is rejected implying that there is no difference in time to diversification between subsidiaries of majors and newly entering independent labels. An explanation as to why this hypothesis was rejected is possibly related to the grouping that was used for the independent variable in this hypothesis. The independent labels (de ipso and de novo) were thought to apply the same reasoning regarding diversification. This turned out to be incorrect. From the results acquired using the original grouping, it can be inferred that subsidiaries of indies, are the fastest when it comes to diversifying their output, whereas the de novo entrants took the longest time. This could also be caused by the fact that the internal competition among sister subsidiaries from larger independent labels might be fiercer because the independent labels have less slack resources to divide than the majors (Birkinshaw et al.,

2005). This phenomenon is also apparent in the movie industry, where nascent ventures compete for essential resources to start the production of movies (Ebbers & Wijnberg, 2012).

The notion of having to compete for resources could lead to them to abandon their specialized focus, and use portfolio management strategies to spread the risk and attempt to increase their returns by diversifying the output. Thereby suggesting that there is a connection between the fast diversification of indie de ipso entrants and the increased levels of output in the short-term, with as objective to convince the parent company that they are worth the investment. This could also explain why de novo entrants are not bothered with fast diversification, as they do not have to answer to a parent company, and therefore are able to pursue their niche focus. Another factor that might prevent de novo entrants from diversifying, is the lack of human capital and resources that are needed for entering a new genre (Rudsenske & Denk, 2005).

44 5.4 Access to Valuable Connections

From the analysis of hypothesis 1b, it was established that the subsidiaries pursued by one of the three major labels have the highest number of average releases. One of the potential reasons for this is that the distribution of recordings is easier for these labels, as they can make use of the integrated distribution systems, thereby leveraging economies of scale (Scott, 1999).

Additionally, looking at the dependent variables containing the Billboard performance of the labels, it can be seen that they also achieve great results, as their releases often reach the chart, and remain there for some time. On the contrary, it was found that de novo entrants barely make it onto the Top 100, and if they make it, it is most likely a one-time occurrence that only lasts a few weeks. Whereas, the subsidiaries of independent labels perform very well. These good results could be partially explained by the extremely successful label Big Machine

Records (established in 2005), owned by the Big Machine Label Group. This label signed

Taylor Swift, who has become one the greatest pop icons of the past decade. This led to extreme successes for the label, with 77 of Swift’s unique songs appearing in the chart (Billboard,

2018a). Since this exceptional success could be considered an outlier, it was removed and the analyses were rerun. The new results show that without this label present in the sample, the subsidiaries of majors outperform the subsidiaries of independents slightly. These predominantly good results attained by subsidiaries of majors are most likely the result of the connections the majors hold with gatekeepers and the control they have over the old media channels, such as radio or television (Leenders et al., 2015). This affects the chart performance because people tend to resort to these relatively old media sources to find new content because of the extreme oversupply on streaming services (Arditi, 2014b).

In addition, it was argued that the old phenomenon of Payola has been revived on the

Internet under the name ‘Playola’ (Arditi, 2017b). The term Payola refers to the payment of cultural gatekeepers, to play certain songs to increase the commercial success of these songs

45 and artists (Coase, 1979). Record labels used to pay radio stations large sums of money to play their music. In the Payola times, the gatekeepers used to be the radio disk jockeys, whereas in these ‘Playola’ times, the gatekeepers are the playlist curators, who select the songs that are included in the most popular playlists (Peoples, 2015). Even though everyone can compile a playlist on a streaming platform, some of them are compiled by major labels, such as the popular Topsify playlists curated by Warner, Digster playlists curated by Universal, and Filtr playlists curated by Sony (Pelly, 2017). Spotify itself also creates playlists that are very influential, and have a large following. Getting onto a renowned playlist will most likely lead to a great number of plays for the song. Subsequently, this has a positive effect on the chart performance because Billboard takes streaming numbers into account (Forde, 2017). But it is an expensive venture with costs estimated to be around $2,000 up to $10,000 for spots in prominent playlists (Flynn, 2018). Therefore, it is likely that this is only a viable option for majors and large indies, which implies that this might make it harder for de novo entrants to attain a sufficient performance.

And finally, hypothesis 3a and 3b are accepted. These predicted that artist-fronted labels that are started de intra were more likely to enter the industry. These labels were contrasted against vanity labels, which are given to established artists as a sign of encouragement by their own record labels. The de intra entrants, however, performed considerably worse than the vanity labels when looking at the average output after entry. These results are similar to hypothesis 1a and 1b. This means that the implied valuable connections established by de intra entrants by being active in the industry before, are not likely to provide a substantial advantage when starting a record label. A possible explanation could be that this established network might be non-existent or not as effective as thought. A clarification might be provided by the fact that many of the de intra entrants in this research did not have great successes in the music industry before entering as a record label. For the analyses, an entrant

46 was classified as a de intra entrant when the founder(s) of the label had been active in the music industry before (producer and/or artist), or when they released their own output. The group of de intra entrants that released their own output might contain artists that have been refused record deals with existing labels, and therefore have taken it upon themselves to start their own label and release their own music. However, record labels are still intermediaries that fulfil the task of gatekeeping (Hirsch, 1969; Caves, 2000). Record labels only offer deals to those artists that they think have the most potential to become a success (Caves, 2003). If an artist is struggling to get a record deal, it might be an indication that their output is not of sufficient quality. The lack of high-quality material could explain the difference in performance between the de intra entrants and the de ipso entrants. Moreover, this would imply that most of the de intra entrants did not have a substantial career in the music industry before they started their label, which infers that they had not yet acquired many valuable connections. Thus, they are likely to enter the industry with little to no real advantage over de novo entrants.

47 6. Conclusion 6.1 Insights

The goal of this study is to research the likelihood of entry of record labels into the music industry and the subsequent performance of these new entrants. It was argued that records labels would become obsolete because of value chain disintermediation. However, the opposite proved to be true, as the labels strengthened their position in the industry by being innovative and adaptive, thereby proving to be a valuable intermediary between the creative input and the commercial output. The analysis of music metadata on record labels acquired through the MusicBrainz database resulted in several main findings.

Firstly, there are still many new record labels entering the industry. The largest group of new entrants are those that start a venture completely independently. Entry has become more attractive to this group because of the decreased barriers to entry and the great returns that are being attained by incumbents. However, the road to success is hard because of the tough competition with incumbents and other new entrants. When contrasted against the de ipso entrants, it is evident that the position of the de novo entrants is unfavourable and that perseverance is necessary for them to achieve great successes. Furthermore, many de intra entrants also have a hard time establishing a sufficient performance. An explanation could be that these vertically integrating artists might be overconfident in their abilities to make it in the industry. It was argued that established record labels still act as quality gatekeepers, thereby only selecting those musical acts that are of a certain quality level.

Secondly, the results showed that the famous distinction between major and independent labels is not as clear as was thought. It was found in the statistical analyses that both types of de ipso entrants have highly similar results on several bases. It was expected that independent labels would initiate specialized subsidiaries more often to provide a favourable climate for their artists. Whereas major labels would apply a portfolio management approach

48 to their subsidiaries, thereby aiming for profit maximization. A possible explanation for these similar results could be found in the theories on mimetic isomorphism. These state that in uncertain environments companies try to imitate the successful players in an industry, which could explain the conformity among the de ipso entrants. That is why I propose that there should be an additional category for ‘mini-majors’, a term introduced in the literature on the movie industry. This terminology would be a more appropriate way to describe those large independent labels that are structured and act similarly to the major labels.

And finally, as was predicted in several previous studies, valuable connections to industry gatekeepers and old media channels might improve the performance of a record label considerably. This could be related to having access to a large budget destined for marketing and promotion so that a preferential position can be established by for example paying money to acquire a spot in a renowned playlist. Thereby suggesting that success as a label in the music industry is partially dependent on whether there are many resources that can be utilized.

6.2 Practical Implications

This study details how in a concentrated environment, there is still room for new firms to start, and informs about the origins of these new ventures. It is critical that the incumbents are aware that new firms are entering, as this could seriously influence the configurations and dynamics of the industry, even though the new entrants might not be able to compete with these incumbents on a direct level. Incumbents should act fast if they want to prevent new entry. This could constitute of making deals with current partners or competitors, so that prospective entrants cannot access certain distribution channels, thereby raising barriers to entry again.

Conversely, incumbents could also look at the opportunities that the new entrants provide, by for example forming strategic partnerships and utilize them to improve their collective

49 performance. The prevention of or the cooperation with new entrants can only be done when the incumbents are informed about the competitive environment.

On the contrary, prospective entrants should also be aware of the current dynamics in an industry. Porter’s Five Forces could provide a convenient framework to get an overview of the competitive environment. Additionally, using portfolio management strategies to reduce the risk and increase the returns, is highly recommended in situations where uncertainty is present, as this might allow the new venture to increase the probability of a performance that is satisfactory. And finally, in the creative industries, connections to industry gatekeepers and mass media channels could be advantageous. Therefore, it is recommended that potential de novo or de intra entrants establish a large network of contacts before entering the industry, as these connections could prove to be very valuable.

6.3 Limitations

The implications of this research are restricted by several limitations. The main limitation is that the research design has been impaired by selection bias. This is a problem that is inherent to the field of strategic management because these studies rely on the selection of a sample that only represents a fraction of the real population (Certo, Busenbark, Woo &

Semademi, 2016). For this study, this implies that, for example, individuals that have thought of starting a record label but did not pursue this option are not observable and are therefore excluded from the analyses. This may lead to compromised results, as individuals will most likely only start a new venture when they expect it to become a success. All the tests in this thesis were affected by this bias. For instance, the analysis of hypothesis 2 is tainted by selection bias because only companies that had already diversified by adopting a second genre that was different from their first genre are included in the sample that is used for testing. This eliminates all the companies that did not diversify up until the end of 2017, thereby

50 compromising the outcome. This selection bias leads to a reduction of the external validity of the study, thereby impairing the representativeness of the sample, and decreasing the possibility to generalize the results to a wider population.

Secondly, the analyses of the hypotheses are done based on information retrieved from an open-source database. This means that the information in this database is added by non- professional individuals, and could therefore, be prone to mistakes. Moreover, the data used to compile the variables for entry mode was also collected on websites which rely on public input.

Although MusicBrainz and Billboard have been used for academic research before, this could still harm the reliability of the results due to individual errors. A related problem is that the information in the database might not be complete, especially referring to the exit year, as this data might be hard to obtain. This might have affected the outcome of the analysis where the short-term and long-term performance was reviewed. Also, in hypothesis 3, vertical integration by de intra entrants is researched. However, only actors that pursue forward vertical integration, namely artists or producers that start their own label, are incorporated in the de intra variable.

Whereas, vertical integration could also be pursued backwards. But because of a lack of information on distributors or manufacturers that pursue vertical integration, backwards integrating entities were omitted from the de intra category, leading to an incomplete analysis.

And finally, another limitation is posed by the approach taken to measure performance.

Conventionally, financial data is used to make inferences about the performance of a company

(Hagel, Brown & Davison, 2010). For a large percentage of the firms, no financial information could be found, likely due to the newness and the small size of most companies. Therefore, it was determined that the number of musical recordings released was the most appropriate measure of performance for this research. However, this measurement is not ideal, since there might be multiple factors that influence the number of recordings released that do not all relate to the performance of an organization.

51 6.4 Future Research Options

There are several possible future research ventures that could be undertaken to deepen the understanding of strategic new entry. Firstly, to get a more complete overview of what influences the entry rate of new firms, additional analyses using hazard or survival models could be performed. These methods control for right-censoring, which implies that those firms that have not yet entered during the observation period are also considered (Haveman, 1993).

This could improve the understanding of the factors that explain new entry, allowing researchers to make more solid inferences about the rate of entry (Özsomer & Cavusgil, 1999).

Secondly, another avenue for future research could consist of applying the entry modes to different industries. Especially considering the uncertainty that is present in creative industries, it would be interesting to see whether an industry with a stable environment provides notably different results. Moreover, an industry where value chain envy plays a less obtrusive role could possibly provide interesting outcomes. Value chain stages that are more covert and where the attained returns are not as publicly acknowledged, should experience less value chain envy. This could lead to less de novo entrants, whereas an influx of de ipso entrants is expected.

And finally, potential future studies that focus on the music industry and record labels could benefit from pursuing a qualitative approach. This could provide insights into the intrinsic motivations, expectations, and reasoning behind the strategic choices made by the new entrants. Also, further research into the financial impact of horizontal and vertical integration could possibly help to explain the likelihood of pursuing specific entry modes as well. Detailed financial information could provide a better understanding of the configurations of the companies, and it could confirm the assumptions that were made about the excess resources, and the availability of these funds for subsidiaries.

52 7. References

ADA. (2018). ADA - INDEPENDENT MUSIC WORLDWIDE SERVICES. Ada-music.com. Retrieved 31 January 2018, from http://ada-music.com/#mission AIM. (2016). An AIM Guide to Starting an Independent Record Label. Association of Independent Music. Retrieved from http://www.musicindie.com/wp-content/uploads/2016/10/how-to-start-an-independent-record-label.pdf Alexander, P. J. (1994a). Entry barriers, release behavior, and multi-product firms in the music recording industry. Review of Industrial Organization, 9(1), 85-98. Alexander, P. J. (1994b). New technology and market structure: Evidence from the music recording industry. Journal of Cultural Economics, 18(2), 113-123. Anderson, C. (2006). The Long Tail. London: Random House. Arditi, D. (2014a). Digital downsizing: The effects of digital music production on labor. Journal of Popular Music Studies, 26(4), 503-520. Arditi, D. (2014b). iTunes: Breaking barriers and building walls. Popular Music and Society, 37(4), 408-424. Arditi, D. (2017a). Digital Subscriptions: The Unending Consumption of Music in the Digital Era. Popular Music and Society, 1-17. Arditi, D. (2017b). Music Everywhere: Setting a Digital Music Trap. Critical Sociology, 1-14. Bain, J. S. (1956). Barriers to new competition (Vol. 3). Cambridge, MA: Harvard University Press. Baum, J. (1996). Organizational ecology. Studying Organization: Theory and Method. 71-108. Billboard. (2018a). Taylor Swift. Billboard. Retrieved 6 April 2018, from https://www.billboard.com/music/taylor- swift Billboard. (2018b). Top 100. Billboard. Retrieved 6 April 2018, from https://www.billboard.com/charts/hot-100 Birkinshaw, J. M., & Morrison, A. J. (1995). Configurations of strategy and structure in subsidiaries of multinational corporations. Journal of international business studies, 26(4), 729-753. Birkinshaw, J., Hood, N., & Young, S. (2005). Subsidiary entrepreneurship, internal and external competitive forces, and subsidiary performance. International business review, 14(2), 227-248. Birley, S., & Westhead, P. (1994). A taxonomy of business start-up reasons and their impact on firm growth and size. Journal of Business Venturing, 9(1), 7-31. Bockstedt, J. C., Kauffman, R. J., & Riggins, F. J. (2006). The move to artist-led on-line music distribution: a theory- based assessment and prospects for structural changes in the digital music market. International Journal of Electronic Commerce, 10(3), 7-38. Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Harvard university press. Bourreau, M., Gensollen, M., Moreau, F., & Waelbroeck, P. (2013). “Selling less of more?” The impact of digitization on record companies. Journal of cultural economics, 37(3), 327-346. Bradley, S. W., Aldrich, H., Shepherd, D. A., & Wiklund, J. (2011). Resources, environmental change, and survival: Asymmetric paths of young independent and subsidiary organizations. Strategic Management Journal, 32(5), 486-509. Bray, E. (2010). So who runs the record labels now?. The Independent. Retrieved 20 February 2018, from http://www.independent.co.uk/arts-entertainment/music/features/so-who-runs-the-record-labels-now- 1946160.htm Burkeman, O. (1999). Forty years of the internet: how the world changed for ever. the Guardian. Retrieved 3 March 2018, from https://www.theguardian.com/technology/2009/oct/23/internet-40-history-arpanet Burnett, R. (1992). The implications of ownership changes on concentration and diversity in the phonogram industry. Communication Research, 19(6), 749-769. Burnett, R. (1996). The global jukebox: The international music industry. Psychology Press. Canetta, R. D., & Winn, J. (2002). Colorado creative music. Entrepreneurship: Theory and Practice, 26(3), 101-115. Capron, L., Dussauge, P., & Mitchell, W. (1998). Resource redeployment following horizontal acquisitions in Europe and North America, 1988-1992. Strategic management journal, 631-661. Carroll, G. R. (1985). Concentration and specialization: Dynamics of niche width in populations of organizations. American journal of sociology, 90(6), 1262-1283. Carroll, G. R., Bigelow, L. S., Seidel, M. D. L., & Tsai, L. B. (1996). The fates of de novo and de alio producers in the American automobile industry 1885–1981. Strategic Management Journal, 17(S1), 117-137. Carroll, G. R., Dobrev, S. D., & Swaminathan, A. (2002). Organizational processes of resource partitioning. Research in organizational behavior, 24, 1-40. Carzo, R., & Yanouzas, J. N. (1969). Effects of flat and tall organization structure. Administrative science quarterly, 178-191. Caves, R. E. (2000). Creative industries: Contracts between art and commerce. Harvard University Press. Caves, R. E. (2003). Contracts between art and commerce. Journal of economic Perspectives, 17(2), 73-83. Caves, R. E., & Porter, M. E. (1977). From entry barriers to mobility barriers: Conjectural decisions and contrived deterrence to new competition. The quarterly journal of economics, 241-261. Certo, S. T., Busenbark, J. R., Woo, H. S., & Semadeni, M. (2016). Sample selection bias and Heckman models in strategic management research. Strategic Management Journal, 37(13), 2639-2657.

53 CISAC. (2018). What We Do. CISAC.org. Retrieved 3 February 2018, from http://www.cisac.org/What-We- Do/Information-Services/IPI Coase, R. H. (1979). Payola in radio and television broadcasting. The Journal of Law and Economics, 22(2), 269-328. Colonna, C. M., Kearns, P. M., & Anderson, J. E. (1993). Electronically produced music and its economic effects on the performing musician and music industry. Journal of Cultural Economics, 17(2), 69-75. Creative Commons. (2018). Public Domain Dedication. Creativecommons.org. Retrieved 18 March 2018, from https://creativecommons.org/publicdomain/zero/1.0/ De Laat, K. (2014). Innovation and diversity redux: Analyzing musical form and content in the American recording industry, 1990–2009. Sociological Forum, 29(3), 673-697. DiMaggio, P., & Powell, W. W. (1983). The iron cage revisited: Collective rationality and institutional isomorphism in organizational fields. American sociological review, 48(2), 147-160. Discogs. (2018a). About Us. Discogs.com. Retrieved 15 February 2018, from https://www.discogs.com/about Discogs. (2018b). Spinnin’ Records. Discogs.com. Retrieved 4 March 2018, from https://www.discogs.com/label/2178-Spinnin-Records?genre=Hip+Hop Dowd, T. J. (2004a). Concentration and diversity revisited: Production logics and the US mainstream recording market, 1940–1990. Social forces, 82(4), 1411-1455. Dowd, T. J. (2004b). Production perspectives in the sociology of music. Poetics, 32(3), 235-246. Ebbers, J. J., & Wijnberg, N. M. (2012). Nascent ventures competing for start-up capital: Matching reputations and investors. Journal of Business Venturing, 27(3), 372-384. Elberse, A. (2010). Bye-bye bundles: The unbundling of music in digital channels. Journal of Marketing, 74(3), 107- 123. Ferraris, P., Auberty, N., & Rao, T. (2008). Music Labels: Striking the Right Chord for Stimulating Revenues. Capgemini. Retrieved from https://www.capgemini.com/gb-en/wp- content/uploads/sites/3/2017/07/tl_Telecom__Media___Entertainment_Insights _Journal_Volume_4.pdf Field, A. (2009). Discovering statistics using SPSS. London, England: SAGE Flynn, J. (2018). Are Spotify playlists the new payola?. Dancing Astronaut. Retrieved 17 April 2018, from https://dancingastronaut.com/2018/03/spotify-playlists-new-payola/ Forde, E. (2017frost). 'They could destroy the album': how Spotify's playlists have changed music for ever. the Guardian. Retrieved 17 April 2018, from https://www.theguardian.com/music/2017/aug/17/they-could- destroy-the-album-how-spotify-playlists-have-changed-music-for-ever Fox, M. (2004). E-commerce business models for the music industry. Popular Music and Society, 27(2), 201-220. Frost, J. (2015). Choosing Between a Nonparametric Test and a Parametric Test. Blog.minitab.com. Retrieved 19 March 2018, from http://blog.minitab.com/blog/adventures-in-statistics-2/choosing-between-a- nonparametric-test-and-a-parametric-test Futrelle, J., & Downie, J. S. (2002). Interdisciplinary communities and research issues in Music Information Retrieval. In ISMIR (Vol. 2, pp. 215-221). Gemser, G., Van Oostrum, M., & Leenders, M. A. (2007). The impact of film reviews on the box office performance of art house versus mainstream motion pictures. Journal of Cultural Economics, 31(1), 43-63. Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non- statisticians. International journal of endocrinology and metabolism, 10(2), 486 Giarratana, M. S. (2008). Missing the starting gun: de alio entry order in new markets, inertia and real option capabilities. European Management Review, 5(2), 115-124. Gompers, P., Kovner, A., & Lerner, J. (2009). Specialization and success: Evidence from venture capital. Journal of Economics & Management Strategy, 18(3), 817-844. Graham, G., Burnes, B., Lewis, G. J., & Langer, J. (2004). The transformation of the music industry supply chain: A major label perspective. International Journal of Operations & Production Management, 24(11), 1087- 1103. Grogan, A. (2011). Today's producers. Engineering & Technology, 6(11), 32-34. GVL. (2018) Labels and Label Codes. GVL.de. Retrieved 3 February 2018, from https://www.gvl.de/en/rights- users/labels-labelcodes Hagel, J., Brown, J. S., & Davison, L. (2010). The best way to measure company performance. Harvard Business Review, 4. Harrigan, K. R. (1984). Formulating vertical integration strategies. Academy of Management review, 9(4), 638-652. Harrigan, K. R. (1985). Exit barriers and vertical integration. Academy of Management Journal, 28(3), 686-697. Haveman, H. A. (1993). Follow the leader: Mimetic isomorphism and entry into new markets. Administrative science quarterly, 593-627. Hemerly, J. (2011). Making metadata: The case of MusicBrainz. Hesmondhalgh, D. (1996). Flexibility, post-Fordism and the music industries. Media, Culture & Society, 18(3), 469- 488. Hesmondhalgh, D. (1998). The British dance music industry: a case study of independent cultural production. British Journal of Sociology, 234-251. Hesmondhalgh, D. (2013) The Cultural Industries. London: Sage. Hirsch, P. M. (1969). The Structure of the Popular Music Industry. Ann Arbor: Survey Research Center, University of

54 Michigan. Hirsch, P. M. (1972). Processing fads and fashions: an organization-set analysis of cultural industry systems. The American Journal of Sociology, 77(4), 639–659. Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2012). Strategic management cases: competitiveness and globalization. Cengage Learning. Hitters, E., & Van de Kamp, M. (2010). Tune in, fade out: Music companies and the classification of domestic music products in the Netherlands. Poetics, 38(5), 461-480. Homan, S., & Gibson, C. (2007). Popular music: Networks, industries and spaces. Media International Australia incorporating Culture and Policy, 123(1), 61-64. Hracs, B. J. (2012). A creative industry in transition: The rise of digitally driven independent music production. Growth and Change, 43(3), 442-461. Hughes, J., & Lang, K. R. (2003). If I had a song: The culture of digital community networks and its impact on the music industry. International Journal on Media Management, 5(3), 180-189. IFPI. (2017). GLOBAL MUSIC REPORT 2017. The Annual State of the Music Industry. Retrieved from http://www.ifpi.org/downloads/GMR2017.pdf Ingham, T. (2017a). Universal music group valued at $22bn – three times what it was worth in 2013 – Music Business Worldwide. Retrieved 8 March 2018, from https://www.musicbusinessworldwide.com/universal-music- group-valued-at-22bn-three-times-what-it-was-worth-in-2013/ Ingham, T. (2017b). Warner buys Spinnin' Records in $100m+ deal - Music Business Worldwide. Music Business Worldwide. Retrieved 5 March 2018, from https://www.musicbusinessworldwide.com/warner-buys-spinnin- records-100m-deal/ IPI. (2018). IPI · IPI. Ipisystem.org. Retrieved 15 February 2018, from https://www.ipisystem.org ISNI. (2018). How ISNI works. isni.org. Retrieved 3 February 2018, from http://www.isni.org/how-isni-works Jones, G. R., & Butler, J. E. (1988). Costs, revenue, and business-level strategy. Academy of Management Review, 13(2), 202-213. Joy, D. (2017). Those Hip Hop Guys: A Brief History of Eminem's Shady Records. Milwaukee Journal Sentinel. Retrieved 22 February 2018, from https://www.jsonline.com/story/entertainment/music/2017/03/14/hip-hop- guys-brief-history-eminems-shady-records/99186742/ Kaye, L. (2015). 10 Infamous Feuds Between Artists And Their Record Labels. The Richest. Retrieved 26 January 2018, from https://www.therichest.com/expensive-lifestyle/money/10-infamous-feuds-between-artists-and- their-record-labels/ Keller, G. (2012). Managerial statistics. South-Western Cengage Learning. Kobalt. (2018). Services. Retrieved 10 April 2018, from https://www.kobaltmusic.com/services Kolar, J. (2017). The impact of digitalisation on the recorded music consumption: An Estonian case study. International Journal of Music Business Research, 6(1), 6-34. Lacey, T. (2014). The Songwriter's Guide to the Galaxy. Kent: PeaChi Publishing Laerd. (2018a). Independent t-test for two samples using SPSS Statistics - Interpreting the Output | Laerd Statistics. Statistics.laerd.com. Retrieved 19 March 2018, from https://statistics.laerd.com/statistical- guides/independent-t-test-statistical-guide.php Laerd. (2018b). Kruskal-Wallis H Test using SPSS Statistics - Interpreting the Output | Laerd Statistics. Statistics.laerd.com. Retrieved 19 March 2018, from https://statistics.laerd.com/spss- tutorials/kruskal-wallis-h-test-using-spss-statistics.php Laerd. (2018c). Mann-Whitney U Test in SPSS Statistics - Interpreting the Output | Laerd Statistics. Statistics.laerd.com. Retrieved 19 March 2018, from https://statistics.laerd.com/spss- tutorials/mann-whitney-u-test-using-spss-statistics-2.php Laerd. (2018d). One-way ANOVA using SPSS Statistics - Interpreting the Output | Laerd Statistics. Statistics.laerd.com. Retrieved 19 March 2018, from https://statistics.laerd.com/statistical- guides/one-way-anova-statistical-guide-4.php Lambkin, M., & Day, G. S. (1989). Evolutionary processes in competitive markets: beyond the product life cycle. The Journal of Marketing, 4-20. Leenders, M. A., Farrell, M. A., Zwaan, K., & ter Bogt, T. F. (2015). How are young music artists configuring their media and sales platforms in the digital age?. Journal of Marketing Management, 31(17-18), 1799-1817. Leurdijk, A., Slot, M., & Nieuwenhuis, O. (2012). Statistical, ecosystems and competitiveness analysis of the media and content industries. European Commission. Li, A. (2011). New Company Vs. New Division. Smallbusiness.chron.com. Retrieved 17 March 2018, from http://smallbusiness.chron.com/new-company-vs-new-division-37955.html Lindvall, H. (2012). Behind the music: What do record labels actually do? You'd be surprised. the Guardian. Retrieved 16 January 2018, from https://www.theguardian.com/music/musicblog/2012/feb/02/behind-music- record-labels Lopes, P. D. (1992). Innovation and diversity in the popular music industry, 1969 to 1990. American sociological review, 56-71. Marshall, C. C., & Shipman, F. M. (2003). Which semantic web?. In Proceedings of the fourteenth ACM conference on Hypertext and hypermedia (pp. 57-66). ACM.

55 Mauboussin, M. J. (2012). The true measures of success. Harvard Business Review, 90(10), 46-56. McKay, M. (2009). What Do I Need to Start a Record Label?. Smallbusiness.chron.com. Retrieved 17 March 2018, from http://smallbusiness.chron.com/need-start-record-label-4752.html McLeod, K. (2005). MP3s are killing home taping: The rise of Internet distribution and its challenge to the major label music monopoly. Popular Music and Society, 28(4), 521-531. Messitte, N. (2015). Five Truly Terrible Record Deals. Forbes.com. Retrieved 17 March 2018, from https://www.forbes.com/sites/nickmessitte/2015/04/30/five-truly-terrible-record-deals-compiled-for-your- convenience/#5a471c415a35 Moder, K. (2007). How to keep the Type I Error Rate in ANOVA if Variances are Heteroscedastic. Austrian Journal of Statistics, 36(3), 179-188. Mol, J. M., Wijnberg, N. M., & Carroll, C. (2005). Value chain envy: explaining new entry and vertical integration in popular music. Journal of Management Studies, 42(2), 251-276. Mol, J., Ming Chiu, M., & Wijnberg, N. (2012). Love Me Tender: new entry in popular music. Journal of Organizational Change Management, 25(1), 88-120. Montgomery, C. A., & Singh, H. (1984). Diversification strategy and systematic risk. Strategic Management Journal, 5(2), 181-191. Moore, F. (2012). The record label is dead: long live the record labels. The Telegraph. Retrieved 26 April 2018, from https://www.telegraph.co.uk/culture/music/9672807/The-record-label-is-dead-long-live-the-record- label.html Mortensen, B. (2010). Choral Music in the Digital Age: Distributing your Music Online. Choral Journal, 50(10), 63- 65. Musgrave, G. (2017). Collaborating to compete: the role of cultural intermediaries in hypercompetition. International Journal of Music Business Research, 6(2), 41-68. MusicBrainz. (2018a). Label - MusicBrainz. Musicbrainz.org. Retrieved 3 February 2018, from https://musicbrainz.org/doc/Label MusicBrainz. (2018b). MusicBrainz Database Schema. Musicbrainz.org. Retrieved 22 February 2018, from https://musicbrainz.org/doc/MusicBrainz_Database/Schema Negus, K. (1999a). Music Genres and Corporate Cultures. London: Routledge. Negus, K. (1999b). The music business and rap: Between the street and the executive suite. Cultural Studies, 13(3), 488-508. Negus, K. (2002). The work of cultural intermediaries and the enduring distance between production and consumption. Cultural studies, 16(4), 501-515. Özsomer, A., & Cavusgil, S. (1999). A dynamic analysis of market entry rates in a global industry: a community ecology perspective. European Journal of Marketing, 33(11/12), 1038-1063. Palepu, K. (1985). Diversification strategy, profit performance and the entropy measure. Strategic management journal, 6(3), 239-255. Passman, D. (2015). All you need to know about the music business. New York: Simon & Schuster. Pehrsson, A. (2006). Business relatedness and performance: A study of managerial perceptions. Strategic Management Journal, 27, 265–282. Pelly, L. (2017). Not All Spotify Playlists Are Created Equal: An Unedited Look Behind The Green Curtain. hypebot. Retrieved 17 April 2018, from http://www.hypebot.com/hypebot/2017/06/secret-lives-of-playlists.html Peltoniemi, M. (2015). Cultural industries: Product–market characteristics, management challenges and industry dynamics. International journal of management reviews, 17(1), 41-68. Peoples, G. (2015). How 'Playola' Is Infiltrating Streaming Services: Pay for Play Is 'Definitely Happening'. Billboard. Retrieved 17 April 2018, from https://www.billboard.com/articles/business/6670475/playola-promotion-streaming-services Peterson, R. A., & Berger, D. G. (1975). Cycles in symbol production: The case of popular music. American sociological review, 158-173. Petkova, A. P., Rindova, V. P., & Gupta, A. K. (2008). How can new ventures build reputation? An exploratory study. Corporate Reputation Review, 11(4), 320-334. Porter, A., Bogdanov, D., & Serra, X. (2016). Mining metadata from the web for AcousticBrainz. In Proceedings of the 3rd International workshop on Digital Libraries for Musicology (pp. 53-56). ACM. Porter, M. E. (1979). competitive forces shape strategy. Harvard Business Review PPLUK. (2018). Further Information on Label Codes. PPLUK.com. Retrieved 3 February 2018, from http://www.ppluk.com/I-Make-Music/Further-information-on-Label- Codes?utm_source=MemberNLMarch&utm_medium=email&utm_campaign=March%20e-update RateYourMusic. (2018). Label groups - Rate Your Music. Rateyourmusic.com. Retrieved 17 February 2018, from https://rateyourmusic.com/wiki/Music:Label+groups Record Union. (2018). Pricing. Record Union. Retrieved 17 April 2018, from https://www.recordunion.com/pricing.html Reis, S., Negro, G., Sorenson, O., Perretti, F., & Lomi, A. (2012). Resource partitioning revisited: evidence from Italian television broadcasting. Industrial and Corporate Change, 22(2), 459-487. Rothenbuhler, E. W., & Dimmick, J. W. (1982). Popular music: Concentration and diversity in the industry, 1974–

56 1980. Journal of Communication, 32(1), 143-149. Rothenbuhler, E. W., & McCourt, T. (2004). The economics of the recording industry. Media economics: Theory and practice, 221-48. Rothschild, K. W. (1947). Price theory and oligopoly. The Economic Journal, 57(227), 299-320. Rudsenske, J. S., & Denk, J. P. (2005). Start an independent record label. Schirmer Trade Books. Rugman, A., & Verbeke, A. (2008). A regional solution to the strategy and structure of multinationals. European Management Journal, 26(5), 305–313. Rutter, P. (2016). The music industry handbook. Routledge. Schneider, M. (2017). Universal Music 2016 Earnings: Streaming Drove Revenue Growth for Recordings, Publishing. Billboard. Retrieved 15 February 2018, from https://www.billboard.com/articles/business/7701980/universal-music-group-2016-earnings-streaming Scott, A. J. (1999). The US recorded music industry: on the relations between organization, location, and creativity in the cultural economy. Environment and Planning A, 31(11), 1965-1984. Shuker, R. (2017). Popular Music: The Key Concepts. London: Routledge Simonton, D. K. (2005). Cinematic creativity and production budgets: Does money make the movie?. The Journal of Creative Behavior, 39(1), 1-15. Stutzbach, A. R. (2011). MusicBrainz. Notes, 68(1), 147-151. Tanriverdi, H., & Lee, C. (2008). Within-industry diversification and firm performance in the presence of network externalities: Evidence from the software industry. Academy of Management Journal, 51(2), 381–397. Tarzijan, J. (2004). Strategic effects of private labels and horizontal integration. The International Review of Retail, Distribution and Consumer Research, 14(3), 321-335. Tremblay, V., & Tremblay C., (2012). New Perspectives on Industrial Organization: With Contributions from Behavioral Economics and Game Theory. Springer Science and Business Media Trust, G. (2013). Ask Billboard: How Does The Hot 100 Work?. Billboard. Retrieved 6 April 2018, from https://www.billboard.com/articles/columns/ask-billboard/5740625/ask-billboard-how-does-the-hot-100- work Tuma, N. (1979). Invoking RATE. DMA Corporation. Palo Alto, CA. Vaccaro, V. L., & Cohn, D. Y. (2004). The evolution of business models and marketing strategies in the music industry. International Journal on Media Management, 6(1-2), 46-58. Vigliensoni, G., Burgoyne, J. A., & Fujinaga, I. (2013). Musicbrainz for The World: The Chilean Experience. In ISMIR(pp. 131-136). Wallis, R., & Malm, K. (1984). Big sounds from small peoples: The music industry in small countries (Vol. 2). Pendragon Pr. White, R. E., & Poynter, T. A. (1984). Strategies for foreign-owned subsidiaries in Canada. Business quarterly, 49(2), 59-69. Wikström, P. (2012). A typology of music distribution models. International Journal of Music Business Research, 1(1), 7-20. WIN. (2017). WINTEL WORLDWIDE INDEPENDENT MARKET REPORT 2017. The Global Economic & Cultural Contribution of the Independent Record Industry. Retrieved from http://winformusic.org/files/WINTEL%202017/WINTEL%202017.pdf York, J. G., & Lenox, M. J. (2014). Exploring the sociocultural determinants of de novo versus de alio entry in emerging industries. Strategic Management Journal, 35(13), 1930-1951. Zhou, Y., & Davis, J. (2005). Open source software reliability model: an empirical approach. In ACM SIGSOFT Software Engineering Notes (Vol. 30, No. 4, pp. 1-6). ACM. Zuckerman, E. W., & Kim, T. Y. (2003). The critical trade-off: identity assignment and box-office success in the feature film industry. Industrial and Corporate Change, 12(1), 27-67.

57 8. Appendices 8.1 Appendix 1 – Multidivisional Majors

Overview of the multidivisional structures of the three major record labels. (note: not all sublabels and divisions are pictured) (MusicBrainz, 2018a; Discogs, 2018a; RateYourMusic, 2018)

58

8.2 Appendix 2 – Lifecycle of Small Labels

Routes of possible development of small record companies by Wallis and Malm (1984, p.157).

59 8.3 Appendix 3 – MusicBrainz Keys

Table overview for the keys available for the entity record label in the MusicBrainz database.

Name The official name of the label. If there are multiple names being used for the same label, the most common is used in the database. URL The URL for the specific page of the record label on the MusicBrainz website MusicBrainz Exclusive identification number for the record label in the database, so that other ID datasets can be connected by comparing the label ID. Label Type The type of label describes the main activity of the label (9 types) • Production referring to record labels producing new records. • Original production refers to a label producing completely new releases. • Reissue production includes record labels specializing in the reissuing of releases that have been produced before. • Bootleg production means that a record label specializes in the release of recordings that were not legally issued by the artists. • Publisher refers to companies that ensure that songwriters and musicians receive their part of the payment when their compositions are used. • Distributors are mainly affiliated with the distribution of records produced by other record labels. • Imprint is strictly a trademark and a front, and is owned by another label. • Holding is a term used to refer to conglomerates or financial entities, which manage a large set of record labels. These do not produce records themselves. • Rights society describes a company that collects royalties on behalf of musicians. Begin Date The begin date is different for some types of labels. Either the date of issuance (y/m/d) of the first release or the date the firm was officially registered. End Date If there is an end date present in the dataset, this means that the record label (y/m/d) went bankrupt, or simply ended their business. Country Code Numerical code that refers exclusively to the country where the label was started. Label Code Most record labels have been assigned a label code, starting with LC followed by 5 digits. This coding was commenced in Germany by the Gesellschaft zur Verwertung von Leistungsschutztechten (GVL, 2018). This code enables the collection of royalties in Germany, and is therefore acquired by record labels that release music in Germany and Europe (PPLUK, 2018). This means that not all record label will have a label code, however the majority of the labels (or parent labels) are active in Europe and Germany. IPI The interested party information (IPI) code is assigned to all the owners of copyrights for protected and public domain. It is assigned to the rightful parties by the International Confederation of Societies of Authors and Composers. The rightful party can be either a natural person or a legal entity (CISAC, 2018). The code contains 11 numbers and is used to identify rights holders for several creative industries, such as music, movie and literature (IPI, 2018). ISNI The International Standard Name Identifier (ISNI) for the label is also included in the database. This number also works as an identifier for the record label. An ISNI is also used for artists and other cultural contributors (e.g. producers) in order to identify the individuals or entities involved in the production of a cultural good (ISNI, 2018). For this identifier also holds that it can be given to a natural person or a legal entity. Date Added The date the entry was added by a contributor Comments Additional comments on the entries (e.g. about the genre of releases)

60 8.4 Appendix 4 – Statistical Analysis Results

8.4.1 Normality tests Table 1. Normality tests for all dependent variables

Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig.

Average releases 0.341 426 0.000 0.419 426 0.000

Time to diversification 0.303 190 0.000 0.714 190 0.000

Average releases 1st 3 years 0.370 392 0.000 0.307 392 0.000 Average releases 2nd 3 years 0.367 340 0.000 0.357 340 0.000

Without electronic 0.338 319 0.000 0.430 319 0.000

Billboard songs 0.369 28 0.000 0.387 28 0.000

Billboard weeks 0.303 28 0.000 0.514 28 0.000

8.4.2 Hypothesis 1a Table 2. Descriptives

Performance n M SD

De ipso (major) 40 5.69 8.14 De ipso (independent) 57 5.2 9.42 De novo 329 1.31 3.06 Total 426 2.24 5.29

Table 3. Chi-squared goodness of fit test

χ2 df Sig 370.41 2 0.000

8.4.3 Hypothesis 1b Table 4. One-way analysis of variance

SS df MS F Sig Between groups 1260.68 2 630.34 25.07 0.000 Within groups 10635.91 423 25.14 Total 11896.6 425

61 Table 5. Levene’s test for equality of variance

F df1 df2 Sig 47.09 2 423 0.000

Table 6. Welch analysis of variance

df F df1 2 Sig 10.18 2 63.45 0.000

Table 7. Non-parametric test: Kruskal-Wallis

H df Sig 46.60 2 0.000

Table 8. Post hoc test: Games-Howell

Classification scheme(I) Classification scheme(J) Mean difference (I-J) Sig De ipso (major) De ipso (independent) 0.487 0.960 De novo 4.380 0.005 De ipso (independent) De ipso (major) -0.487 0.960 De novo 3.892 0.009 De novo De ipso (major) -4.380 0.005 De ipso (independent) -3.892 0.009

8.4.4 Hypothesis 1c Table 9. Descriptives

Performance n M SD

De ipso (major) 40 5.69 8.14

De ipso (independent) 57 5.2 9.42

Table 10. Binomial test

n Observed proportion Test proportion Sig De ipso (major) 40 0.41 0.50 0.104 De ipso (independent) 57 0.59 Total 97 1.00

62 8.4.5 Hypothesis 1d Table 11. Independent samples t-test Levene's F Levene's sig t df Sig Equal variances assumed 0.26 0.61 0.27 95.00 0.792 Equal variances not assumed 0.27 90.89 0.786

Table 12. Non-parametric test: Mann Whitney

U Z Sig 909.00 -1.69 0.090

8.4.6 Additional analysis H1: Short-term and Long-term Table 13. Descriptives short-term and long-term

Performance n M SD De ipso (major) 37 3.94 5.43

De ipso (independent) 54 4.81 11.45

De novo 301 1.30 2.22 Total 392 2.03 5.11

Performance

n M SD

De ipso (major) 31 4.55 6.43 De ipso (independent) 47 4.64 10.80 De novo 262 0.87 2.31 Total 340 1.72 5.10

Table 14. One-way ANOVA short-term and long-term

SS df MS F Sig Between groups 712.70 2 356.35 14.61 0.000 Within groups 9486.49 389 24.39 Total 10199.19 391

SS df MS F Sig Between groups 836.30 2 418.15 17.64 0.000 Within groups 7990.29 337 23.71 Total 8826.60 339

63

Table 15. Levene’s test short-term and long-term

Short-term F df1 df2 Sig 25.33 2 389 0.000

Long-term F df1 df2 Sig 36.87 2 337 0.000

Table 16. Welch ANOVA short-term and long-term

Short-term F df1 df2 Sig 6.64 2 58.86 0.003

Long-term F df1 df2 Sig 7.64 2 49.62 0.001

Table 17. Non-parametric: Kruskal-Wallis short-term and long-term

Short-term H df Sig 39.39 2 0.000

Long-term H df Sig 34.07 2 0.000

8.4.7 Additional analysis H1: No Electronic

Table 18. Descriptives

Performance n M SD

De ipso (major) 37 5.29 7.76

De ipso (independent) 40 6.76 10.85 De novo 242 1.20 2.40 Total 319 2.37 5.48

Table 19. One-way ANOVA

SS df MS F Sig Between groups 1415.36 2 707.68 27.48 0.000 Within groups 8138.60 316 25.75 Total 9551.95 318

64 Table 20. Levene’s test, Welch ANOVA, and Kruskal-Wallis test

df Levene’s F df1 2 Sig 58.51 2 316 0.000

Welch ANOVA F df1 df2 Sig 10.03 2 51.06 0.000

Kruskal-Wallis H df Sig 41.01 2 0.000

8.4.8 Additional analysis H1: Billboard Songs and Weeks

Table 21. Descriptives BB songs and weeks

Performance n M SD De ipso (major) 15 4.47 5.37 De ipso (independent) 7 42.71 65.11 De novo 6 1.67 1.21

Total 28 13.43 35.43

Performance n M SD De ipso (major) 15 77.67 24.64

De ipso (independent) 7 289.71 403.17 De novo 6 20.33 25.19 Total 28 118.39 227.22

Table 22. One-way ANOVA BB songs and weeks

SS df MS F Sig Between groups 8038.36 2 4019.18 3.88 0.034 Within groups 25844.50 25 1033.78 Total 33882.86 27

SS df MS F Sig Between groups 228030.58 2 144015.29 3.61 0.049 Within groups 1105970.10 25 44238.80 Total 1394000.68 27

65 Table 23. Levene’s test BB songs and weeks

BB songs F df1 df2 Sig 17.59 2 25 0.000

BB weeks F df1 df2 Sig 5.27 2 25 0.012

Table 24. Welch ANOVA BB songs and weeks

BB songs F df1 df2 Sig 5.01 2 11.83 0.042

BB weeks F df1 df2 Sig 4.12 2 12.02 0.048

Table 25. Kruskal-Wallis BB songs and weeks

BB songs H df Sig

9.59 2 0.000

BB weeks H df Sig

7.40 2 0.025

8.4.9 Hypothesis 2 Table 26. Descriptives

Performance n M SD

Major 38 1.00 1.51

Independent 152 1.35 1.87

Table 27. Binomial test

n Observed proportion Test proportion Sig Major 38 0.20 0.50 0.00 Independent 152 0.80 Total 190 1.00

66 Table 28. Independent samples t-test Levene's F Levene's sig t df Sig Equal variances assumed 4.00 0.04 -1.07 188 0.288 Equal variances not assumed -1.21 68.52 0.230

Table 29. Non-parametric: Mann-Whitney

U Z Sig 2646.00 -0.85 0.379

8.4.10 Additional analysis H2: Original Grouping Table 30. Descriptives

Performance

n M SD

De ipso (major) 38 1.00 1.51 De ipso (independent) 25 0.60 1.04 De novo 127 1.50 1.96 Total 190 1.28 1.81

Table 31. One-way ANOVA SS df MS F Sig Between groups 20.47 2 10.23 3.212 0.042 Within groups 595.75 187 3.19 Total 616.22 189

Table 32. Levene’s test

df F df1 2 Sig 6.375 2 187 0.002

Table 33. Welch ANOVA

df F df1 2 Sig 5.500 2 70.50 0.006

Table 34. Non-parametric: Kruskal-Wallis

H df Sig 6.02 2 0.049

67 8.4.11 Hypothesis 3a Table 35. Descriptives Performance

n M SD

De intra 131 1.47 2.72 De ipso (vanity label) 23 4.32 6.34

Table 36. Binomial test

n Observed proportion Test proportion Sig De intra 23 0.85 0.50 0.00 De ipso (vanity label) 131 0.15 Total 154 1.00

8.4.12 Hypothesis 3b

Table 37. Independent samples t-test

Levene's F Levene's sig t df Sig Equal variances assumed 28.13 0.00 -3.61 152.00 0.000 Equal variances not assumed -2.12 23.44 0.045

Table 38. Non-parametric test: Mann-Whitney

U Z Sig 1065.00 -2.24 0.025

68