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Music piracy: investigating the effects of illegal

file sharing on the performance of emerging

artists

Master Thesis

MSc in Business Administration Entrepreneurship and Innovation track

Faculty of Economics and Business

Student: Emilio Giavarini (11615249) Thesis Supervisor: dr. Michele Piazzai Date: 22/06/2018

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Statement of originality

This document is written by Student Emilio Giavarini who declares to take full responsibility for the contents of this document.

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

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

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Table of Contents

1. Introduction………5

2. Literature Review………..8

2.1 Explaining piracy………...8

2.2 The Napster case………9

2.3 Piracy as an element that impacts on record sales………11

2.4 Piracy as an element that affects the performance of emerging artists compared to well-known performers……….13

2.5 Piracy as an element that changed music distribution………..15

2.6 Research question, conceptual framework and hypotheses………..16

3. Research Methodology ………18

3.1 Sample and data gathering………..19

3.2 Variables……….20 3.3 Data sources ………...23 4. Results ………...25 4.1. Descriptive statistics………..25 4.2. Correlation matrix ……….27 4.3. Hypotheses testing……….29 4.4. Limitations……….35

5. Discussion and conclusions………...37

Acknowledgements………39

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Abstract

Ever since Napster was first released in 1999, the impact of illegal file sharing on the recording industry has been the focus of intense debate. While majority of academic findings have analyzed the effects of music piracy on record sales, there is a gap in existing literature about the impact of piracy on emerging performers. I conduct an empirical analysis by comparing a sample of songs released before Napster was shut down in 2001 with a sample of songs released after that, in order to assess the effects of music piracy on the performance of emerging artists who released their debut album. In addition, I distinguish between musicians who released their content with a major record label and those who did so with an independent one. The results of this study suggest that there is no statistically significant difference between the two sample populations. I conclude the thesis by stating implications for young performers seen as young entrepreneurs.

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1. Introduction

"I don't like people stealing my music" is what was published in a statement by Dr. Dre when the rap superstar decided to sue Napster for having “built a business based on large-scale piracy" in the year 2000 (Nelson, 2000). But how exactly did music piracy impact on the overall performance of artists at the beginning of the century? To answer this question, we have to distinguish between emerging artists and well-known performers, which benefit (or get harmed) in different ways from illegal file sharing. Before record labels had a chance to adjust to the emerging model of digital music distribution, Napster arrived and disrupted the industry like nothing before (Menn, 2003). Napster’s peer-to-peer application launched in 1999, focusing on the sharing of MP3 music files and allowing users to download tracks with relative ease (McCourt & Burkart, 2003). Just like Dr. Dre, the notorious heavy metal band Metallica and other performers also sued Napster, ultimately resulting in its closure (Doan, 2000). While Napster’s success may have been short-lived, it created the biggest turning point in the music industry in decades: it made music free and accessible to everyone and, most of all, it gave emerging performers the chance to gain quick popularity (Ericsson, 2011).

The effect of piracy on sales revenue of recorded music is still a subject of great interest between scholars and members of the creative industries. Reasonably performers and record labels have to protect their interests by stating that music piracy has affected negatively the industry causing a great deal of losses in terms of revenue and jobs (Liebowitz, 2008). On the other hand, there is plenty of research done by scholars and supported by consumer associations that music piracy is whatsoever uninfluential (Oberholzer-gee & Strumpf, 2007) or even positive for the industry

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6 (Gopal, Bhattacharjee, & Sanders, 2006; Peitz & Waelbroeck, 2006). Forbes revealed that the European Commission even paid for a study on how piracy impacts the sales of copyrighted music, books, video games, and movies. But the EU never shared the report, possibly because it determined that there is no evidence that piracy is a major problem (Woollacott, 2017). In addition to state entities and record labels, musicians themselves seem to be divided over whether file sharing is good or bad, as a survey of American musicians done by the Pew Research Center shows (Rainie & Madden, 2004).

At first, I wanted to analyze this topic from the record labels’ perspective and focus on the impact piracy has on record sales, but then I noticed there is an abundance of research made on that argument. Therefore, I decided to look at the emerging performers as emerging entrepreneurs who try to arise in the music industry, and to see what effect piracy has on their performance.

This thesis will try to analyze all the consequences of illegal file sharing on the performance of emerging artists. A quantitative approach was chosen as preferred research methodology considering that large quantities of statistical data had to be analyzed. This study will gather empirical information about debut songs which were released before and after Napster’s shutdown, in order to evaluate the impact music piracy had on young artists. In addition, my research will distinguish between artists who released their content with a major record label and those who did it with an independent one.

What my research will try to do is find out if illegal music sharing throughout the beginning of the century was harmful or beneficial to young artists. By doing that I will consider artists as entrepreneurs, and see if an environment, marked by a big share of files distributed illegally for free, could have a positive or negative influence on the entrepreneur’s performance. This could be

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7 of great value for future managerial contributions as it may help predict if a young artist should or should not release his initial content for free. The potential future contribution will be to assess if an emerging performer should release his content free of charge while he is at the beginning of his career in order to promote his products.

The structure of the thesis will be as follows. Chapter 1 is the introduction to the subject. Chapter 2 is the Literature Review which will provide a discussion of prior literature that built this study. It will start by explaining piracy in general, then continue by describing the Napster case, and conclude by analyzing piracy in its different aspects. Chapter 3 is the Research Methodology which will explain the quantitative methodology of data collection, afterwards the research will take place. Chapter 4 will show the results explained in order to elaborate the study’s findings and verify the hypotheses. Chapter 5 will be Discussion and Conclusions which consist of a summary of the results explained, limitations and future implications.

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2. Literature Review

2.1 Explaining piracy

In order to fully comprehend how illegal file sharing affected emerging artists’ performance around the years 2000-2001 we must first analyze music piracy and understand why Napster was such a big disruptor. Innovation is at the core of every successful business, but it is particularly evident in the music industry where different business models have been succeeding to each other. New forms of distributing content and making revenue have been replacing old ones in the last thirty years. The music distribution sector has changed dramatically in the last fifty years (Teece, 2010). Before the 1980’s music distribution happened through vinyl records, then compact cassettes and then CDs. Yet the biggest change in music distribution was the invention of the MP3 which opened the way to the digital era: music no longer had to be physically stored on a CD, but it could be easily transferred via the Internet (Wagner, Rose, Baccarella, & Voigt, 2015). This boom in the distribution of music was also followed by controversies surrounding copyright infringement and gave birth to what is known as music piracy (Bhattacharjee, Gopal, & Sanders, 2003).

Advances in the technological field, particularly in Internet connection and digital compression (MP3), helped effectively increase online sharing of music data (Bhattacharjee et al., 2003). Therefore, the act of piracy had become excessively popular at the beginning of the century evolving even into a habit for many consumers. At the time of the research done by Oberholzer‐ Gee & Strumpf (2010), the authors state that more than 60% of Internet traffic consists of

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9 consumers sharing digital data. File sharing erupted into the public consciousness in 1999, with the release of the software program Napster (Blackburn, 2004).

2.2 The Napster case

Napster was founded in 1999 by Shawn Fanning, a Northeastern University student, and Sean Parker, a software developer and entrepreneur (Howard, 2011). Initially it was programmed to be a peer-to-peer file-exchange network for university students but eventually it grew larger and became more and more popular. Napster was specifically designed for sharing digital music files in the MP3 format, thus providing an easy way for thousands of internet users to gain access to a large database of audio files for free (Harris, 2018). Napster was launched the same year it was founded, and it rapidly rose in popularity revealing its great potential and user-friendly design. At the peak of its popularity, 80 million users were registered on its network (Harris, 2018). The Northwestern University had to ban Napster from its campus due to bandwidth issues: the popular service was simply using a disproportionate amount of the university’s bandwidth, not leaving much for the rest of the students using the campus’s network (Presto 2000).

The reason Napster was such a big disruptor was that it did not have a centralized server, meaning that any user could upload anything he or she wanted, and any user could download anything that was uploaded by other users. This innovative model led to the creation of a huge database of songs where almost everything could be found and downloaded for free. Such a disruptive technology couldn’t go unnoticed and Napster was soon under the radar of the Recording Industry Association of America (the RIAA is the association responsible for protecting the interests and rights of the major record labels), which accused the peer-to-peer service of not controlling the transfer of copyrighted material across its network (Harris, 2018). The RIAA filed a lawsuit against Napster

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10 in 2000 which resulted in a long line of battles in court, won by the RIAA mainly due to a federal law passed two years earlier (Blackburn, 2004).

The Digital Millennium Copyright Act is a U.S. federal law passed in 1998 that made major changes to the previous legislation in terms of regulating copyrighted material (Congress, U. S., 1998). It was backed up by many software and intellectual property firms as it undoubtedly favored their copyright protection. The DMCA empowered any person who claims to be the owner of copyrighted material to request the removal from the Internet of said material without having to go to a judge (Congress, U. S., 1998). The only necessary action the owner of copyrighted material has to undertake is write a formal letter to the Internet Service Provider demanding that the ISP remove the protected intellectual property. The DMCA also states that in case of failure to remove the copyrighted content the ISP becomes co-infringer (Congress, U. S., 1998). The controversial aspect of this law is that it puts the owners of copyrighted material in a position of advantage granting them the undisputed power to decide if an intellectual property can stay online or must be taken down, thus making this law a rare point of agreement between free-speech civil libertarians and the music industry (Kravets, 2008).

In August of 2000 the Recording Industry Association of America, represented in court through its multitude of record labels, filed a lawsuit against Napster for copyright infringement (A&M RECORDS, INC. v. Napster, Inc., 2000). The key question in the Napster trial was whether the practice of sharing and downloading MP3 files actually helped or hurt the market of copyrighted works (Spitz, & Hunter, 2005). In 2001, the United States Court of Appeals for the Ninth Circuit held that the peer-to-peer file-sharing service Napster would be held liable for contributory and vicarious infringement of copyright (Case Study: A&M Records, Inc. v. Napster, Inc., 2013). As

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11 a result, the court granted RIAA’s injunction against Napster and the file-sharing service had to close down for good on the 11th of July 2001. While Napster cannot be considered a story of success, during its two years of activity it was so popular and disruptive that it changed the music industry forever.

2.3 Piracy as an element that impacts on record sales

In the last twenty years the music industry has been greatly impacted by all the emerging technologies. Music-related technological improvements such as the MP3 format and peer-to-peer sharing sites like Napster have gained massive popularity in a quick period of time (Menn, 2003). The Recording Industry Association of America claims that peer-to-peer sharing has negatively impacted on their record sales, dropping 5% from 2000 to 2001 (Dubosson-Torbay, Pigneur, & Usunier, 2004). After the International Federation of the Phonographic Industry (IFPI) released a figure indicating $4.2 billion as the global value of pirated music in 2000 (IFPI, 2001), the RIAA started a war on illegal file sharing. At first it succeeded by shutting down Napster, but it soon realized that file sharing was such a popular phenomenon that many other sites offered the same service. After Napster’s shutdown, there was a short period of inactivity of other peer-to-peer sharing services as they had to restructure themselves in order not to be also sued (Ripeanu & Foster, 2002). Gnutella, a peer-to-peer network, and LimeWire, its main peer-to-peer client, were founded in 2000 but had still to gain popularity. What Gnutella learned from the Napster experience is that having a centralized network is the best way to get prosecuted and shut down. As a result, Gnutella was the first decentralized peer-to-peer file-sharing network of its kind, leading to other, later networks adopting the model (Ripeanu & Foster, 2002). These events brought up a debate which still today is cause of controversy among scholars: is piracy harmful

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12 for record sales? Whether music piracy affected record sales is a topical issue and the debate is still going on. What I found out by analyzing past literature is that there is still no clear answer to this research question. For a large chuck of scholars there is a clear negative impact of piracy on record sales.

To illustrate the effects of illegal file sharing, Stan Liebowitz (2004) calculates the impact of downloading MP3s rather than buying a CD and, consequently, discovers that piracy causes significant harm to the record industry. Likewise, after having executed a qualitative research based on the answers of 15.000 people, Zentner (2006) found out that illegal downloading reduces the probability of buying music, thus affecting negatively record sales. Years later, Liebowitz performs an empirical examination of music piracy by analyzing the values of record sales in 99 American cities before and after file sharing (this approach is similar to my research methodology), thus concluding that illegal downloading causes drops in music sales revenue (Liebowitz, 2008). A similar research was done by Bender and Wang (2009) but instead of analyzing U.S. cities, the authors implemented their study on a country-level. They calculated music sales revenue in fifty-three countries which experienced music piracy in different levels. The results of their research confirmed that music piracy had a detrimental effect on sales revenue. A more in-depth analysis was performed by Oberholzer-Gee and Strumpf (2007), where they link sales revenue to detailed records of transfers of digital music files. Contrary to previous findings, the two authors conclude that file sharing has had no statistically significant effect on purchases of albums in their sample. Similarly to my research, the two experts use data from the Billboard music charts.

Another study that uses data from the Billboard charts is the one done by Bhattacharjee et al. (2007). The authors calculate an album’s survival rate by considering the total number of weeks it

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13 appears on the Billboard 200 chart and they observe the number of files being shared for each album in that time segment. Their findings suggest that file sharing does not hurt the survival of top-ranking albums, while it has a negative impact on the survival of low-ranking ones. An interesting aspect of Bhattacharjee et al.’s research, which is also performed in my study, is the demarcation between major and independent record label. The researchers explain that albums produced by minor labels have experienced a benefit from file sharing, in terms of album survival on charts. This curious division between major and independent record label brought to the formulation of my second and third hypotheses.

But some of the most recent research brought up theories regarding piracy as a positive influence on record sales. Peitz and Waelbroeck (2006) explain that the negative impact of piracy on sales may be overcompensated by a positive one called sampling effect. The sampling effect occurs when consumers download a song or album to see if they like it and then buy it. This way consumers can make more informed purchasing decisions and are willing to spend for the original although they could download it for free. By constructing a model in which the consumer has the choice to either buy or download a product, the two authors explain that the sampling effect dominates over the decision not to buy the product. Their research methodology is based on creating a model with a utility equation for the consumer and for the music distribution firm.

2.4 Piracy as an element that affects the performance of emerging artists compared to

well-known performers

As stated earlier, it is important to note that the effects of illegal file sharing on the sales or performance cannot be considered the same throughout artists of different popularity. Therefore,

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14 in order to have a coherent depiction of the effects of piracy, one must distinguish artists based on their ex-ante popularity.

Literature regarding whether music piracy is beneficial for established or lesser-known artists is also divided. Lee (2018) explains that file sharing activity has a more negative effect on digital sales rather than on physical sales. This happens because there is a higher disposition to substitute digital piracy for digital purchases, rather than physical ones. The author makes a correlation between sales of albums, using data provided by Nielsen SoundScan, and piracy, using data from a private file-sharing website. However, what is interesting of his research, is that the effects differ for artists of different quality and popularity. Established artists’ sales decrease, while younger artists’ sales actually increase with additional file-sharing activity. The author explains this phenomenon using two opposing effects. The first one is called capacity effect in which file sharing activity increases users’ supply of shareable files thus making the user more inclined in illegally downloading the song for free. But there is also an opposite aspect, called the word-of-mouth effect, in which file sharing increases awareness, decreases uncertainty, and therefore increases demand in all markets, including the legal one. Therefore, for artists with an established reputation, the capacity effect overcomes the word-of-mouth effect and the result is negative. But for emerging artists the word-of-mouth effect is far greater than the capacity one, resulting in a benefit for them (Lee, 2018).

Gopal, Bhattacharjee and Sanders (2006) explain the sampling effect stating that file-sharing services which consumers can sample through, increase the number of customers willing to pay for the product they have sampled. To illustrate further, the authors clarify that file-sharing services that give you the option to sample a song before buying it, have a beneficial effect only on

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lesser-15 known artists, thus threatening the “superstars”. The researchers conclude by affirming that piracy leads to discovering emerging artists, therefore harming well-known artists’ sales. For their empirical analysis Gopal et al. use the Billboard ranking charts to assess the relationship between the superstar phenomenon and file sharing.

There is an interesting research gap about this topic that is also confirmed by contradictions in the field. Contrary to what Lee (2018) and Gopal et al. (2006) affirm about piracy being of benefit to emerging artists, there is a research done by (Piolatto & Schuett, 2012) which claims the exact opposite. The authors assert that file sharing has different effects on artists depending on their popularity. Moreover, they explain that piracy has a beneficial effect on popular artists when side revenues (live performances, merchandizing, participation in TV shows) are critical. Similarly, Hammond (2014), by analyzing files shared through a BitTorrent protocol, explains that file sharing benefits more established artists signed with a major record label.

2.5 Piracy as an element that changed music distribution

There might be an abundance of theories whether music piracy is positive or negative for labels and artists, but the fact that music piracy has changed the way music is distributed is a fact (Wagner, Rose, Baccarella, & Voigt, 2015). Easley (2005) examines some of the ethical issues that arise in responding to innovations that are perceived to be threatening, in particular with respect to music piracy. The author claims that music piracy actually played a role in pushing record labels into adopting new distribution technologies. The same author (Easley, R. F., Michel, J. G., & Devaraj, 2003) explains how the threat posed by MP3 has provided all record labels with motivation to transform their business model in order to mitigate potential damage from this disruptive technology. If there is a bright side of situation in the battle between the music industry

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16 and MP3-based music piracy, it is that the latter has pushed the key players of music distribution to embrace new technologies. Another interesting research is one which also confirms this theory (Danaher, Dhanasobhon, Smith, & Telang, 2010). The study explains that online music distribution, precisely paid subscriptions, dissuade consumers from pirating, as the cost of pirating equals or is even more than the cost of the subscription. The authors theorize that the great availability of digital channels for media distribution will cannibalize old channels for physical sales. Moreover, the researchers state that these legitimate digital channels of distribution will destroy the illegitimate ones, consequently reducing piracy (Danaher et al., 2010).

2.6 Research question, conceptual framework and hypotheses

After a thorough analysis of existing literature which focuses mainly on the effects of piracy on record sales (from the record label point of view) and on well-known performers I decided to pursue a research which aims to discover the impact of an environment marked by piracy on emerging artists. The goal of my research is to understand whether young musicians who released their debut album or song performed better during or after the Napster era. Ultimately, this study aims to deduce if emerging performers should be in favor or contrary to the strict legislation surrounding piracy.

My thesis will try to fill this research gap by investigating the effects of illegal music file sharing on the performance of emerging artists, while distinguishing between musicians produced with a major label and those produced with an independent one. Research question: what were the effects of music piracy on the performance of emerging artists during and after the Napster age?

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17 Figure 1. Conceptual framework

Hypotheses as analyzed in the Research Methodology section:

H1A: Debut songs released by emerging artists before Napster’s shutdown stay on the Hot 100

chart more weeks than those released after its shutdown.

H1B: Debut songs released by emerging artists before Napster’s shutdown have a better

performance score than those released after its shutdown.

H2A: Debut songs released by emerging artists with an independent record label before Napster’s

shutdown stay on the Hot 100 chart more weeks than those released after its shutdown.

H2B: Debut songs released by emerging artists with an independent record label before Napster’s

shutdown have a better performance score than those released after its shutdown.

H3A: Debut songs released by emerging artists with a major record label before Napster’s

shutdown stay on the Hot 100 chart more weeks than those released after its shutdown.

H3B: Debut songs released by emerging artists with a major record label before Napster’s

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3. Research methodology

In the following part the relationship between music piracy and the performance of the debut single of emerging artists is analyzed. In particular I will analyze the performance of debut singles of two different groups: one group released their songs before Napster was shut down, and the other group released their songs after Napster was shut down. The preferred approach is an empirical analysis, as numerical data is expected to provide clear measurable evidence for any relation between the observed variables. In this study I will quantify the performance of debut singles based on the number of weeks a song stays on the Billboard Hot 100 chart and on a model I created for this research. I did not consider well-known artists as the assumption is that their performance is not affected by piracy since they are already popular.

Before Napster was shut down, it was one of the few if not the only provider of such peer-to-peer services. As mentioned in the Literature Review, file-sharing networks other than Napster had a smaller market share and weren’t that popular at the time of this study (Gallagher, 2001). For the purpose of this research I assumed that before Napster was shut down, downloading music was much easier, so there was a great deal more illegal file sharing. After Napster was shut down in July 2001, other file-sharing services had still to emerge and take over the U.S. market (Gallagher, 2001). Therefore, before other services offered what Napster did, there was a period of seven months during which downloading music was harder for the consumer, resulting in less piracy in the American market.

The first hypothesis of my research is that there is a positive relationship between music piracy pre and post Napster Shutdown and the performance of debut singles, calculated through the

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19 number of weeks a song stays on the Billboard Hot 100 chart and a model I created which takes into account both the number of weeks on chart and the maximum rank it reaches. My second and third hypotheses state also that this relationship is moderated by the type of label the artist was produced by (major or independent).

3.1 Sample and data gathering

Data for the study was collected by considering all songs, performed by artists who are releasing their debut single or album in the periods 9th of December 2000 to 7th of July 2001 and 15th of July 2001 to 9th of February 2002 (both periods are 30 weeks long), which have made at least one appearance on the Billboard Hot 100 chart. Entries of artists who released a debut single outside of the US were not considered for the sample. This filtering brought to a sample composed of 53 debut singles in the last seven months of Napster and 46 debut songs in the first seven months after Napster’s shutdown, for a total of 99 entries.

In February 2002 the Gnutella network started to gain popularity as LimeWire, a peer-to-peer file-sharing client based on Gnutella, became free and open source (Ghosemajumder, 2002). This technological development, combined to other peer-to-peer clients already active, made music piracy re-emerge in the United States and worldwide after an seven-month dormant period.

The main logic that shaped how data was collected, is the fact that during the period from August 2001 to February 2002 music piracy was very much less active, as consumers had little choice which file-sharing site to use. Napster was dead, and its competitors had still to prove themselves, trying not to be also involved in a federal trial.

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Period Start Date End Date Nº of weeks Entries

Pre-Shutdown 9th of December 2000 7th of July 2001 30 53

Post-Shutdown 15th of July 2001 9th of February 2002 30 46

Total 60 99

Table 1. Sampling periods

3.2 Variables

Independent variable

The independent variable of this research is music piracy, which is measured by two contrasting periods. The first period is before Napster was shut down in July 2001 and the second after Napster’s shutdown. As I stated earlier, the assumption is that during the Napster age piracy was more active than after Napster’s shutdown. I divide the sample of songs by being part of either group 1 (pre Napster shutdown) or group 2 (post Napster shutdown). For the analysis of the hypothesis a dummy variable was created on SPSS: pre or post shutdown with the values “Pre” (0) and “Post” (1).

Pre and Post Napster’s shutdown

Frequency Percent Valid Percent Cumulative Percent

Valid Pre 53 53.5 53.5 53.5

Post 46 46.5 46.5 100.0

Total 99 100.0 100.0

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Dependent variables

The main dependent variable of my study is Weeks, which consists on the total number of weeks a song stays on the Billboard Hot 100 chart. On SPSS this is a scale variable which goes from a minimum of 1 to a maximum of 54 weeks for the best performing entry. The number of weeks a song stays on the Hot 100 chart can be considered a meaningful variable since it represents for how long a song has been in the one hundred top-selling and most listened songs in the U.S. market. The only drawback of this variable is the fact that out of 99 entries, 29 stayed on the chart for the exact same number of weeks, which is twenty weeks. To try to solve this complication I created a model, which considers also the peak position a song manages to reach on the Hot 100 chart.

The second dependent variable is the performance of the debut single of emerging artists as measured by a performance score which is based on a model I created for this research. To measure the performance of each single on the Billboard Hot 100 chart, a model was created in order to take into account both the number of weeks the song appeared on the chart and the peak position it managed to reach. Through this model we are able to make a more detailed and in-depth analysis of the performance of a song. This alternative method of calculating the performance of a single makes it easier to compare all those songs who have the same value for the number of weeks (e.g., 29 singles stayed on the charts for exactly 20 weeks). With the help of this model we can distinguish these 29 entries as each one will have a different performance score based on the peak rank also. The formula of the performance model is:

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22 It is important to note that a bonus was given to two of the entries as they peaked the 1st place

position on the Billboard chart. These are Fallin’ by Alicia Keys and Butterfly by Crazy Town which stayed at the top rank for six and two weeks respectively.

The secondary dependent variables of this study are debut position, which is simply the position a song reaches in its first week on the chart, and peak position, which is the maximum ranking the single reaches.

Moderator variables

The first moderator variable in my research is the record label which the artist released the song with. A record label can be either a major label (Universal, Sony, Warner) or an independent label. This variable is of great significance as it helps understand the different effect of piracy on the two types of label. A major label has access to much more funds than an indie label in order to promote a debut artist and his album. For the analysis of the hypothesis a dummy variable was created with the values “indie” (0) and “major” (1).

Record Label

Frequency Percent Valid Percent Cumulative Percent

Valid Indie 20 20.2 20.2 20.2

Major 79 79.8 79.8 100.0

Total 99 100.0 100.0

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23 The second moderator variable is music genre. I will try to find out how music genre moderates the effect of piracy on performance. For this variable there are six music genres which in SPSS have values (1,2,3,4,5,6).

Control Variables

The control variable in my research is the number of performers, which could be a solo performer, a duo or a band (a band is every group with more than two performers). I will keep this variable under control as I assumed it will have no influence on the outcome of the research. On SPSS this variable is coded as “solo” (1), “duo” (2) and “band” (3).

3.3 Data sources

The data collected for this research is secondary data taken from two main sources: Billboard.com and the MusicBrainz Database.

Data needed in order to fill in the population sample was gathered from Billboard.com, and more specifically from the Hot 100 chart. The Billboard Hot 100 chart ranks the best performing 100 singles from each week across all genres. Performance is calculated as a sum of three elements: radio airplay audience impressions, as measured by Nielsen Music, sales data as compiled by Nielsen Music, and streaming activity data provided by online music sources (Billboard.com, 2018). The Billboard Hot 100 chart is considered a credible source as it is managed by the Billboard magazine, which was founded in 1894 and since then has been considered a legitimate source of music knowledge. The sales source for the Billboard music charts is Nielsen SoundScan, which is the largest source of sales records in the music industry and is widely cited by numerous publications and broadcasters as the standard for music industry measurement (Nielsen.com,

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24 2018). In the Literature Review many examples of studies who use the Billboard charts can be found.

Details about each song were gathered by the MusicBrainz Database, which is one of the biggest online music databases in the world with more than two million registered releases (musicbrainz.org, 2018). The MusicBrainz Database provided details about release date, label, genre and group or solo performer.

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4. Results

This chapter will discuss the results of the analysis that has been performed. This research makes use of SPSS 24.0 to analyze the data and the results were obtained from its output file. This chapter will be structured as follows: firstly, descriptive statistics are presented, secondly, a correlation matrix is performed. Then the hypotheses are tested, and results are shown. Finally, the limitations of this research will be produced.

4.1. Descriptive Statistics

We can observe from Table 1. that there is a wide range between the maximum and minimum Score, thus resulting in a high standard deviation but that will not affect our hypotheses testing. This wide range for the Score variable is the result of the fact that there is also a wide range between the Weeks and Peak Range variables. The average number of weeks a debut song released in 2001 stayed on the Hot 100 chart is 18.71, which is below the median of 20 (29 songs out of 99 stayed on the chart for exactly 20 weeks), meaning that there are fewer entries who exceed that value.

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Median

Weeks 99 1 54 18.71 10.206 20

Score 99 .301 107.764 34.089 21.579 82

Peak Rank 99 1 99 40.78 30.323 35

Debut Rank 99 8 100 82.21 15.646 35.117

Valid N (listwise) 99

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Distribution

As we can observe from Figure 2. the Weeks variable data is not distributed normally. A skewness value of 0.77 indicates that data is moderately skewed to the right meaning that it is not symmetrically distributed and that there are more entries who scored less than the mode (mode is 20 weeks with 29 entries scoring it). Another inference we can make about the distribution of our main variable comes from the kurtosis. A kurtosis value of 1.05 shows that there is a moderate positive kurtosis, meaning that data is distributed more centrally, which is also due to the 29 entries that stayed on the chart for 20 weeks each.

Introducing the Score variable is an attempt to make the data more normally distributed, but it only helps to a limited extent. We can observe that the skewness remains the same (0.8), meaning that there are more entries who scored less than the mode (mode is around 39 with 14 entries scoring from 38.1 to 39.8). The kurtosis value diminishes from 1.05 to 0.92 making the distribution less central and more normalized.

It is also interesting to notice the negative Peak Rank kurtosis (-1.19) which indicates that different songs reached very different chart positions.

Descriptive Statistics

N Mean Skewness Kurtosis Statistic Statistic Statistic Std. Error Statistic Std. Error Weeks 99 18.71 .771 .243 1.045 .481 Score 99 34.08914 .798 .243 .918 .481 Peak Rank 99 40.78 .382 .243 -1.192 .481 Debut Rank 99 82.21 -1.528 .243 4.634 .481 Valid N (listwise) 99

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27 Figure 2. Distribution of the population sample of the Weeks variable

4.2. Correlation matrix

As we can observe from the values of the correlation matrix, there is a very high correlation between the variables Score and Weeks (0.993) and between Score and Peak Rank (-0.81), but that is an obvious result as the performance score is calculated by multiplying the number of weeks by the logarithm of (101 – peak rank).

It can also be noticed that there is a high correlation (-0.757) between the number of weeks a song stays on the chart and the maximum position it reaches. That is because if a song usually stays for

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28 a long period of time on the chart it also ranks high. There are almost no cases of singles that stay on the chart for a short period and gain a high rank or stay on the chart for a long time and have a low rank. Needless to say, the correlation between these two variables is negative because Peak Rank is inverted compared to Weeks (rankings go from 100 to 1, while weeks increase).

There is a medium correlation between Debut Rank and Peak Rank (0.335) meaning that most of the songs that reach a high rank in the chart, perform well during their first week, and obviously all of the songs that reach a poor maximum rank, perform poorly in their first week on the chart. All other variables have a low correlation index.

Correlations

Weeks Score Peak Rank Debut Rank Weeks Pearson Correlation 1

Sig. (2-tailed)

N 99

Score Pearson Correlation .993** 1

Sig. (2-tailed) .000

N 99 99

Peak Rank Pearson Correlation -.757** -.810** 1

Sig. (2-tailed) .000 .000

N 99 99 99

Debut Rank Pearson Correlation -.121 -.146 .335** 1

Sig. (2-tailed) .232 .148 .001

N 99 99 99 99

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

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29

4.3. Hypotheses testing

In order to test the hypotheses several t-tests were performed. The independent-samples t-test was chosen because it is the most accurate way to compare the means of two independent populations. The t-test for the difference in means is a hypothesis test that compares the null hypothesis that the means for both groups are equal, versus the alternative hypothesis that the means are not equal (2-tail) or that the mean for one of the groups is larger than the mean for the other group (1-(2-tail).

H0: µ1 = µ2 (the two population means are equal)

H1: µ1 ≠ µ2 (the two population means are not equal)

Table 7. The two hypotheses of a t-test

We can use the independent-samples t-test as the two populations are independent and categorical, and there is no causal relationship between them. An additional requirement for the t-test is that the dependent variables should be measured on a continuous scale, as happens to be our case.

4.3.1. Hypothesis 1

Hypothesis H1A tests the difference of means of the Weeks variable of the two populations. An

independent-samples t-test was performed in order to test the average number of weeks on the Hot 100 chart of the debut songs which were released before Napster’s shutdown and those released after its shutdown. We can observe that the average number of weeks of songs released during the Napster age is 18.4 weeks, while the average number of weeks of songs released in a pirate-free environment is 19.07 weeks.

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30 For my research I will use the first row of this table (the one named Equal variances assumed) since the p-value of Levene’s Test for Equality is 0.903. The decision rule of Levene’s Test states that if the p-value is bigger than 0.05 (significance level) there is no significant difference in the variances of the two groups, meaning that we will accept the null hypothesis and assume equal variances.

Now that we have assumed equal variances, we will proceed with the analysis of the p-value of the t-test for equality of means. We can observe from Table 2. that the p-value equals 0.747 which is higher than the 0.05 significance level and indicates that we must accept the null hypotheses of equal means. The high p-value is given as a result of a high standard deviation of the two groups. Even though there is a mean difference of 0.669 weeks between the two groups we must conclude (considering the high p-value) that there is no significant difference between the means of the two populations, consequently rejecting hypothesis H1A.

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean Weeks Pre 53 18.40 10.311 1.416

Post 46 19.07 10.186 1.502 Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Weeks Equal variances

assumed

.015 .903 -.324 97 .747 -.669 2.066 -4.770 3.432

Equal variances not assumed

-.324 95.363 .747 -.669 2.064 -4.767 3.429

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31 Since I couldn’t conclude that there is a significant difference between the two groups regarding the average number of weeks a song stays on the chart, I decided to perform a t-test using a model I created which takes into account also the peak rank of the debut single. As explained earlier, my model simply multiplies the number of weeks by the logarithm of the rank score (rank score equals 101 – rank) thus giving a way to differentiate between those 29 entries with the same number of weeks.

Hypothesis H1B tests the difference of means of the Score variable of the two populations

performed by an independent-samples t-test. The average score of the pre-shutdown songs is 33.57 while that of the post-shutdown is 34.68. Since the p-value of Levene’s Test for Equality is 0.879, we will accept the null hypothesis and assume equal variances. As we can observe the p-value of the t-test equals 0.8 which is higher than the 0.05 significance level and means that we must accept the null hypotheses of equal means again.

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean Score Pre 53 33.572 21.981 3.019

Post 46 34.684 21.334 3.145 Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Score Equal variances

assumed

.023 .879 -.255 97 .800 -1.112 4.369 -9.784 7.559

Equal variances not assumed

-.255 95.769 .799 -1.112 4.36 -9.767 7.542

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32

4.3.2. Hypothesis 2

The second hypothesis of this study proposes that debut songs released by emerging artists with an independent record label before Napster’s shutdown stay on the chart longer than those released after its shutdown. The average number of weeks on chart of the first group is 15.4, while the average of the second group is 19.

Among the debut songs released in 2001 with an independent record label (N =20), there was no statistically significant difference between pre-shutdown releases (M = 15.4, SD = 15.77) and post-shutdown releases (M=19, SD = 12.92), as the p-value is higher than 0.05 (p-value=0.583). Therefore, we fail to reject the null hypothesis that there is no difference in the means.

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean Weeks Pre 10 15.40 15.771 4.987

Post 10 19.00 12.919 4.085 Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Weeks Equal variances

assumed

.095 .761 -.558 18 .583 -3.600 6.447 -17.144 9.944

Equal variances not assumed

-.558 17.328 .584 -3.600 6.447 -17.182 9.982

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33 The H2B hypothesis of this study proposes that debut songs released by emerging artists with an

independent record label before Napster’s shutdown have a better performance than those released after its shutdown. The average score of the first group is 27.76, while the average score of the second group is 35.53. Levene’s test suggests that we assume equal variances and the high p-value of the t-test (0.564) shows that the difference in means cannot be considered significant.

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean Score Pre 10 27.759 32.428 10.254

Post 10 35.525 26.312 8.320 Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Score Equal variances

assumed

.104 .751 -.588 18 .564 -7.765 13.205 -35.51 19.979

Equal variances not assumed

-.588 17.26 .564 -7.765 13.205 -35.594 20.091

Table 11. t-test for songs released with independent label for the Score variable

4.3.3. Hypothesis 3

The H3A Hypothesis suggests that debut songs released by emerging artists with a major record

label before Napster’s shutdown stay on the Hot 100 chart more weeks than those released after its shutdown. Among the debut songs released in 2001 with a major record label (N =79), the t-test did not present a statistically significant difference between pre-shutdown releases (M = 19.09, SD = 8.701) and post-shutdown releases (M=19.08, SD = 9.512). The p-value of 0.996 is not only

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34 higher than 0.05 so that we have to accept the null hypothesis that there is no significant difference in the means, but it is approximately equal to 1, meaning that the two means are statistically almost the same.

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean

Weeks Pre 43 19.09 8.701 1.327

Post 36 19.08 9.512 1.585

Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Weeks Equal variances

assumed

.028 .868 .005 77 .996 .010 2.051 -4.074 4.094

Equal variances not assumed

.005 71.83 .996 .010 2.067 -4.112 4.131

Table 12. t-test for songs released with major label for the Weeks variable

The H3B hypothesis of this research argues that debut songs released by emerging artists with a

major record label before Napster’s shutdown have a better performance than those released after its shutdown. The average score of the first group is 34.92, while the average score of the second is 34.45. As in the case of hypothesis H3A the p-value of 0.915 indicates that the two means are

virtually the same. We can conclude the hypotheses testing by affirming that all three hypotheses are rejected.

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35

Independent Samples Test

Pre Post N Mean Std. Deviation Std. Error Mean Score Pre 43 34.924 19.051 2.905

Post 36 34.451 20.171 3.361 Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper Score Equal variances

assumed

.009 .926 .107 77 .915 .472 4.420 -8.329 9.275

Equal variances not assumed

.106 72.91 .916 .472 4.443 -8.382 9.328

Table 13. t-test for songs released with major label for the Score variable

4.4 Limitations

Despite the fact that this research was carefully prepared, I am still aware of its (unavoidable) limitations and shortcomings. The limitations of a research methodology are determined by internal and external validity. The internal validity of a study refers to the degree to which statistical inferences can be made based on the measures and the empirical method used in the research, while the external validity of a study concerns the extent to which the internally valid results are applicable to other environments and populations (Stock & Watson, 2012).

In general, this research can be considered internally valid as there was no manipulation of the independent variable. The only construct of this study that may not be representative of real life is the score model I created to assess the performance of songs (the model takes into account both number of weeks and maximum rank gained in the charts). Future researchers could create a more detailed model. Another element of this research which could make it less internally valid is the

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36 small population, due to the fact that there are only one hundred songs in the Hot 100 chart. It would have been more interesting to see songs ranking from first to 300th.

The external validity of this study depends on the assumption I based my research on. The main supposition of this thesis is that in the seven months following Napster’s shutdown, piracy activity was much less intense compared to what it was during the Napster era. Apart from this assumption, my research can be considered also externally valid.

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37

5. Discussion and conclusions

This thesis has investigated the effects produced by illegal file sharing on the performance of emerging artists in the United States, during a time period which was marked by the rise and fall of Napster. I considered two different periods, one while Napster was active and one when Napster had already been shut down, to estimate these effects. Using data about a song’s performance obtained by the Billboard charts, I discover that releasing a debut song before or after Napster’s shutdown has had no statistically significant effect on the performance of the artist. This result is true for both, artists who release their song with a major label and those who release it with an independent one. Regarding songs released with an independent record label, if we observe only the means, we can note a difference between the two periods, but the excessively high variance (which consecutively results in an excessively high p-value) indicates that this difference in means cannot be considered statistically significant. These results are consistent with by Oberholzer-Gee and Strumpf’s findings (2007), in which the authors find no statistically significant effect of illegal file sharing on album sales.

However, it is important to note some of the main limitations of this study. The first limitation is undoubtedly the fact that, even though this research has linked music piracy to an artist’s performance, it has not been able to assess the total impact of piracy on an emerging performer’s career. There might be more factors that need to be taken into consideration, thus more research is needed in this area. Another limitation is caused by the relatively small sample size. The accuracy of this study’s results might have been improved if the population sample was composed of more entries.

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38 The implications for real-world practice of this research suggest that artists who are releasing their debut album or song are neither favored nor harmed by the presence of music piracy, consequently young musicians should not participate in activism against piracy fueled by major record labels and other entities in favor of protecting copyrighted material. Even though the results of this research did not confirm the theory that Napster favored emerging artists, by helping them spread their content, what we can deduct from this study is that a young musician, who sees himself as a young entrepreneur, should release his content independently of whether his surroundings are marked by piracy or not.

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39

Acknowledgements

I would like to express my gratitude to everybody who supported me through the process and completion of my Master thesis. First and foremost, I would like to thank my supervisor Michele Piazzai, who has given me precious insights and has always provided a qualified response to all my questions and doubts. Michele’s guidance steered me in the right direction throughout my research and writing process, while always allowing this paper to be my own work. Finally, I would like to say special thanks to my father, my mother and my sister, who have inspired me to pursue a master’s degree, as challenging as it may be.

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40

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