Surviving the charts A research on the influence of label size on album performances.

Wouter Derksen 10075070 Thesis Seminar Business Studies Academic year: 2013-2014 Supervisor: Frederik Situmeang Semester 2, Block 3

Abstract The music industry faced a lot of changes in the past ten years. Physical album sales extremely decreased and online album sales and streaming services increased extensively. The way we buy and listen to our favourite music has altered, affecting the influences labels have on album sales. It is important for labels to adapt their ways of making an album sell. During this research a combination of data on album lifespan and discography information per artist is used, acquired by using the Billboard Top 200 Charts and datasets from Rovi. We assessed the influence major labels have on album performances and how genre changes affect this relationship, by conducting regression analysis. As a result we found that as an artist it is more beneficial to be released through a major label since this leads to a longer album lifespan, higher peak rank and higher debut rank in the Billboard Charts. Genre changes in the new released product compared to the rest of the discography seem to lead to a higher peak and debut rank.

I would like to thank Caroline Benelux for giving me the opportunity to do an internship at their office in Amsterdam and giving me insights in the music industry. Also many thanks to Michele Piazzai for supporting me with advice and helping me obtain the data used in this paper.

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Introduction Every year over 30.000 albums are being released, just a few of these albums make it to the charts and sell successfully. This small part of albums that make it to the charts, bring in the biggest share of profit for the record company (Bhattacharjee et al., 2007, p. 1361). Most of the album sales are a result of the ‘committed fan base’ purchasing the album on the day of the release, being the main influence for the debut rank of the album in the charts. When an artist stays in the charts for a longer time, it is a reflection of the acceptation of the album by the ‘non-committed fans’ (Bhattacharjee et al., 2007, p.1361). For an artist it is a big achievement to make it into the charts, but even better to stay in the charts and reach superstardom. The music industry is changing and with that the way to measure the popularity and success of an artist. Not only the way we consume music has changed also the way artists reach stardom and thus successful album sales, has changed (Helmink, 2014, March 30). Downloads and streams give a better rending of how popular an artist actually is. Some people might think the more talented an artist is the more success this artist will have, unfortunately we cannot use talent as a predicting variable for success in sales. Franck & Neusch describe this in their research; talent is hard to determine and since people can have different perceptions on what talent is, the measurement of talent will cause problems (2012, p.205). Whether superstardom is reached by use of talent or marketing, it is one of the biggest influences on album sale. Besides this process, also the label plays a big part influencing the sales as they take care of all the marketing, promotion and retail of an album. Currently three record labels are dominating the music industry, namely: Universal Music Group, Warner Brothers and Sony-BMG, we refer to these labels as ‘major’ labels. Being signed by one of these labels seems as an achievement for beginning artists, surprisingly it was shown that the effect of being released by one of the major record labels has fallen over time. Independent labels have been closing the gap with the major labels over the past few years. Using new branding strategies such as word of mouth and use of new technologies such as social media lead to a bigger reach of potential customers (Bhattacharjee et al., 2007, p.1372). Therefor the main focus of this study is on album sales, and how these sales are influenced by different factors with the main factor being label size. It would be interesting to see whether this shows in the actual album sales and how this effect is influenced by different factors such as previous performances and genre changes. The Billboard Top 200 Chart will be our reflection of album sales; the more an artist sells the higher the position in the chart. In order to find specific info on how label size influences album sales we do not just look at minor versus major size, but also whether

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previous albums were released by a minor or a major label, whether the artist released other products prior to the album and if the new album is of a different genre than the previous products. A more in-depth explanation of these variables will be given in the conceptual model and the methodology. To provide an answer to this research topic, quantitative research in the form of OLS regression analysis will be the overall design of this paper, in order to estimate the parameters in a linear regression. We will perform several regressions in which we will each time add more variables to get a more detailed view of what variables influence the album sales the most. The structure of the paper will be the following; next is a literature review, explaining previously studied issues related to this subject. Followed by the conceptual model, explaining the constructs and variables used in this research. After that, the methodology is explained, followed by the results and the discussion section in which we show the regression analysis and elaborate on them. Finally, the conclusion part answers the research question and shows what steps have to be taken for future research.

Literature review In this section we will give an overview of the previous conducted literature that is relevant to support this research. Over the past few years a discussion raised, whether or not talent determines an artist to reach super stardom and in order reach the charts by successful album sales. The discussion argues whether high quality music sells better than music containing less talent and quality but is released by a bigger label. In other words, arguing whether music that is promoted and marketed more because of the bigger budgets and use of bigger networks as a result of being released by a major label, will sell more. Rosen showed that a small difference in talent could translate into a large difference in earnings. He describes the phenomenon of superstars, “wherein relatively small number of people earn enormous amounts of money and dominate the activities in which they engage” (1981, p.845). This is what we call, the superstar effect. Rosen stated that the people that marked the concentration of output do have the most talent (1981, p.847). As a contradiction to this statement, Adler explained that a hierarchy in income could exist without a hierarchy in talent: “Stardom is a market device to economize on learning costs in activities where ‘the more you know the more you enjoy.’ Thus stardom may be independent of the existence of a hierarchy of talent” (Adler, 1985, p.208). The main argument in his research is that the phenomenon of stardom exists where consumption requires knowledge. Meaning that consumers do not like to search for their products and

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mostly decide to purchase products that are easy to reach. Also people would like to talk with other people about their purchases and want to be a part of a certain group and reach a level of recognition. This explains that superstars will remain superstars, even though an artist that is less known might be more talented than the superstar, consumers will have to go through a bigger search in order to find this artist. Most consumers choose not to do so and stick to the superstar (Adler, 1985, p.208). This is what we call the positive network externalities of popularity (Franck & Neusch, 2012, p.202). Positive network externalities explain why it is easier for a big artist to remain selling albums when the first albums are released through a major label. This type of consumer behavior makes it hard to measure when a song is a good song and if it will be successful in terms of sales. Previous study showed that no measure of a song’s quality can be a reliable predictor for success and that social influences determine the revenue distribution among artists and create the unpredictability of success in terms of sales (Franck & Neusch, 2012, p.205). Not every person has the same taste in music, people like different artists simply because of their taste and interest. Therefore it is hard to determine the quality of music and artists, because people’s opinions might differ. Franck & Neusch explain that it is quite simple to show the estimated earnings functions and to point out the differences in talent, although the independent measure of talent is untestable (Franck & Neusch, 2012, p. 205). The first issue in talent determination is its validity. One person might love a certain artist while another person might dislike the same artist. In arts there is an intrinsically subjective component to quality, which makes it difficult to get a valid determination of talent since people simply have different tastes in music. Besides validating talent, it is also hard to measure talent. “Even if we would agree that charisma on stage is the most important ability of a pop star, scholars would still face the difficulty of capturing charisma on a metrical scale. Talent is inherent and thus hard to quantify” (Franck & Neusch, 2012, p.205). These problems create limitations for our empirical research (Connolly & Krueger, 2006). Therefore, quality and talent should be completely disregarded during this research, in order to avert any biasedness. As discussed, previous literature shows the effect superstar status has on album sales, in fact an album released by a superstar is estimated to survive 35% longer on the charts than an album by a non-superstar (Bhattacharjee et al., 2007, p. 1372). The question is how can a label create this effect and successfully sell albums. Our study focuses mainly on how label size affects album sales and because of the use of the Billboard Top 200 Charts, it will be mostly applicable to the superstar market. Therefore are the results not generalizable to any

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niche market within the music industry. A detailed explanation of this can be found in the conceptual model and the methodology. Besides the superstar effect on sales, Bhattacharjee et al. also showed that albums promoted by major labels tend to survive longer in the charts than albums promoted by an independent label. In fact albums promoted by independent labels tend to have a survival duration 23% less than those promoted by major labels (2007, p.1368). The interesting fact of their research is that they showed that although independent albums sell less than major albums, independent labels succeeded in making albums survive longer in the chart than ever before and are closing the gap with the major labels. This creates an interesting new research topic, since finding out why it is that independent labels are doing better now, results in a way to describe what leads to successfully selling an album. One explanation by Bhattaacharjee et al. is that a label should succeed in enlisting superstars and profit by prerelease marketing in order to impact the debut rank of an album in the charts. Another explanation would be that independent labels embrace the use of the new technology regarding social media to reach out to new potential customers and fans. Giving the independent more distribution power and able them to compete more with the major labels (2007, p.1372). So far this previous research shows that there is an interesting discussion on what makes an album sell. Rosen defined the superstar effect and found that relatively few producers gain increasing market shares and receive most of the revenues in the market. Adler observed that this is not necessarily due to superior talent. A number of later researchers suggested that many other factors might contribute to the initial advantage that result into successful sales. Besides, it was showed that independent labels gain more influence on the successful sales of an album. To continue with this thought it would be interesting to see how the achievement of successful album sales is dependent on the influence of the size of the record label and additionally how this effect shows differences across genres. Therefore, we would like to propose the following research question:

What is the effect of label size upon the chart lifespan of an album? - How do genre changes moderate this relationship?

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Conceptual Model In order to conduct this research we first have to clarify how we will determine the lifespan of an album. We decided to use the ‘US Billboard Top 200 Charts’ as an indicator for the sales lifespan of an album. The higher the sales of an album are, the higher the album will be ranked in the chart. Since sales numbers are classified data for most record labels, we use this chart to reflect album sales. ‘Album lifespan’ therefore simply stands for the amount of weeks an album will stay in the Billboard Top 200 Chart. We may assume that when it leaves the chart the album sales decreased to a negligible amount. We created a dataset of all the US Billboard Top 200 Charts from 1985 till 2014. This dataset includes the dates, album names, artist names and label name for each album. With this database it is possible to measure the lifecycle of an album, simply the time between the first appearance of the album in the chart and the last appearance. We may consider the album lifecycle as the time between ‘birth’ when it enters the chart, and ‘death’ when it leaves the chart (Bradlow & Fader, 2001, p.369). In order to minimize limitations we will start the sampling period from 2001, since after 2001 online sharing developed to a more popular level through downloading, sharing and the opening of digital music stores (Bhattacharjee et al., 2007, p.1361). These developments of digital music sharing changed the way music is being consumed, therefore we start looking at the data from 2001 onwards since we are interested in the new developments of music sales. For an artist and a label, the goal is to reach the highest position possible in the Billboard chart. Therefore we will use the lifespan of an album in the chart as an equivalent to album sales (Bhattacharjee et al., 2007, p.1360). This album chart lifespan will be used as the main dependent variable during this research. For the independent variable we will have our main focus on label size, as mentioned, the label names are also part of the dataset. There are three major labels that dominate the music market: Sony-BMG, Universal Music Group and Warner Brothers (see appendix). We will continue to refer to these labels as ‘major labels’ (Bhattacharjee et al., 2007, p.1361). Since we are doing research on album sales we decided that whenever a label gets distribution, promotional or marketing support by one of the three major labels in the industry, we named these labels ‘major’. We are not taking in consideration if a label is independent in A&R, simply because the distribution and promotional power of a major label influences sales a lot. The main differences between major labels and minor labels, which we will call “independent labels”, is the control in recording, distributing and promotion of albums, beside that it is simply given that major labels have a bigger budget and more financial resources to reach more consumers (Bhattacharjee et al., 2007, p.1362). To make the

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relationship between a major label and an independent label that has a link with a major label and therefore in this research is labeled “major” clearer, we created this overview. The arrows show the link between each label:

Figure 1: “The label network.”

Universal Music Group

Caroline International Caroline Distribution

Caroline other (incl. Australia, Caroline Benelux 120 labels worldwide Germany, Scandinavia)

*This table is solely to show a major labels network. Caroline Records is used as an example since this is the label where I did the internship. Source: Caroline Benelux.

Besides label size we will use multiple other independent and control variables to see how the album lifespan changes. As additional independent variables we will use the following: whether the focal product shows a completely new genre compared to the total discography and whether the focal product presents multiple genres or not. To control the regression analysis for errors and limitations we use the following control-variables for each album: The discography size of the artist, the amount of products that are previously released by a major label, the amount of previous released products that are albums, genre changes within the discography and average number of genres per product in the discography. Bhattacharjee et al. show an interesting development in their paper, namely that since the developments in digital music sharing increased and the music consuming changed, the effect of debut rank of chart success has increased and that the effect of being released as an artist by one of the major labels has fallen (2007, p.1361). This conclusion by Bhattacharjee shows the relevant and interesting development in the music industry. The industry is

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changing and we like to see what this means for the power distribution between major labels and independent labels.

Therefore we set the following hypotheses additional to the research question:

H1.a: Products released by a major record label will have a longer lifespan than products released by an independent record label.

H1.b: Products released by a major record label will have a higher peak rank than products released by an independent record label.

H1.c: Products released by a major record label will have a higher debut rank than products released by an independent record label.

H2.a: Genre change in the focal product will have a positive moderating role on the relationship between label size and album lifespan.

H2.b: Genre change in the focal product will have a positive moderating role on the relationship between label size and peak rank.

H2.c: Genre change in the focal product will have a positive moderating role on the relationship between label size and debut rank.

H3.a: Multi-genre products will have a positive moderating role on the relationship between label size and album lifespan.

H3.b: Multi-genre products will have a positive moderating role on the relationship between label size and peak rank.

H3.c: Multi-genre products will have a positive moderating role on the relationship between label size and debut rank.

Figure 2: “The Conceptual Model.”

Genre change Peak rank

Label size Album lifespan

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Previous Debut rank performance (control)

Conceptual Limitations Unfortunately there are some limitations to this concept; we have to account for errors and therefore set control variables. The position of an album in the Billboard chart could be depending on multiple variables, it is not just the promotion and marketing power by the label that determines the success. The historical success of an artist could play a big part in the success of their newest release. Grammys, award nominations, collaborations with superstars and other recognitions for an artists talent and skills should therefore be set as a control variable to make sure that the outcome of this research is solely measuring the influence of label size on the lifespan of an album (Elberse, 2007, p.118). In order we set the control variables mentioned above, due to the limitations of a bachelor thesis we cannot control for all external factors influences album sales. Since we will be using the Billboard Top 200 Chart we will only focus on artists that already made a name for them selves. Beginning artists or smaller artists will not be included in this research, which means that this research is not generalizable to niche markets or other markets besides the ‘popular music’ industry.

Methodology The overall design of the study will consist of quantitative research in the form of OLS regression analysis, in order to estimate the parameters in a linear regression. The analysis will be on a sector and department level. The main focus will be on the products that make it into the charts, therefore niche markets within the music industry are not taken in consideration during this research. The variables will be measured by using the “Billboard Top 200 Chart” database. The album lifetime will be based on the amount of weeks the album is in the chart. The chart information from Billboard magazine is based on sales, they have been doing so since 1913. This makes Billboard a very respected magazine and reliable variable used within the music industry (Bhattacharjee et al., 2007, p.1360). It is a significant achievement for artists to reach the Billboard Chart, this is recognition to their work. This appearance therefore has a lot of influences on profits of an album (Bradlow & Fader, 2001, p. 369).

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Strengths and limitations In its turn quantitative research and the use of OLS regression analysis has its advantages and disadvantages. The data collection when using this process is relatively quick compared to quantitative research and also the analysis of the data is relatively less time consuming. As a researcher you have more control during the analysis since you can eliminate influences of certain variables and allow multiple cause-and-effect relationships between multiple variables. This makes it possible to see how much influence a certain variable actually has on a relationship with the dependent variable. Finally the outcome of the research is less biased than a quantitative research, since the influence of the researcher in conducting the analysis is minimum. Unfortunately every research method contains limitations. The categories and theories used by the researcher during the quantitative analysis might not reflect the real situation. Also since the focus of the research is on the hypotheses that are established at the beginning of the research, the researcher might miss phenomena occurring in the real life situation. Besides this, the focus on the hypotheses could also create another issue, namely that the results might be too abstract to apply to local situations and specific problems (www.southalabama.edu, nd).

Sample We randomly selected a sample of 160 albums out of a database that contained 11.911 albums, which made it into the Billboard Top 200 Chart during the period January 2002 – March 2014. To do so we used an online random number generator (www.random.org). We found a distribution of 30%/ 70% respectively independent label and major label. This is a good estimation of the real world in which the distribution is approximately 25%/ 75%. While making the selection we adjusted for errors, when an artist already was in the sample and was drawn again we disregarded the name and simply drew again. This way we gained a sample with 160 different artists. After filtering for any complications within in this sample, due to IT limitations, we found a useable sample of 119 products. For our sample we collected peak rank, debut rank and amount of weeks (dependent variables), by using the billboard charts as dataset. We used ‘Rovi’ to obtain the independent

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variable as well as previous performances per artist, such as previous releases and whether a major label or a minor released these previous products. Besides that we also collected the genre changes within the discography per artist and how many different genre types the artist released throughout the time period. ‘Rovi Corporation’ is a United States-based company that analyses entertainment data for digital entertainment services. Rovi for example provides the album data that you can find when buying an album in ITunes, such as genre, type of music, writer (rovicorp.com, nd). Also when collecting this data, we made sure the sample did not contain any error samples that might be missing in one or the other dataset as a cause of IT errors.

Results As mentioned before in the methodology a sample of 119 albums was obtained in order to conduct the analysis. Of which, 75% of the albums were released by a major label and 25% by an independent label, reflecting the music industry, as these percentages correspond with the market as it is in real life. After collecting the necessary variable data, by using the Billboard Top 200 Chart and Rovi, we conducted linear regressions using the ordinary least squared method. In this section the results of these regressions can be found, leading to answering the hypotheses. Including a data description and the correlations between the variables.

Data description. Since the conceptual model shows three dependent variables of which “album lifespan” is the main focus, we divide the results per dependent variable, resulting in three different regression tables, which can be found in the appendix. In total we used 12 models to obtain our results in order to answer the hypotheses and the research question. Every dependent variable has four models:

o Control regression, only the control variables (model 1, 5 and 9). o Control variables + main independent variable, which is label size (model 2, 6 and 10).

o Control variables + main independent variable + genre change & multi- genre (model 3, 7 and 11).

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o Control variables + main independent variable + genre change & multi- genre + moderating effect of genre change & multi-genre (model 4, 8 and 12).

Before implementing the regressions we looked at the significance of the variables that control for the previous performance of the artist, as set at the beginning of the research. After dismissing the control variables with no or too little significance we used the following control variables in the regressions: ‘Discography size’, ‘amount of previous products released by a major label’, ‘amount of previous products that are albums’ and ‘the average number of genres per product in the discography’. Since we expect these variables to have an influence on the relationship between label size and the dependent variables we included them as control variables. Not including these variables would lead to a result that does not show resemblance with a real life situation. In table 1in the appendix an overview of the variables is given. We include ‘genre changes within discography’ since we tried to use this as a regressor, unfortunately this control variable did not show any significance during the regression with just the control variables. Therefor this variable only shows the coefficient, SE and beta in the control regressions. For variables that indicate whether the database shows a certain characteristic, we used in the dataset a ‘1’ to identify albums that observe the characteristic and a ‘0’ when the characteristic was not observed. Such as, the variable, “the label is major” and “the focal product is multi-genre”. It also has to be noted that the outcome of the regression describing the influence on debut rank and peak rank should be read different than the regression describing the influence on the album lifespan. The highest possible position to reach in the chart is #1 and the lowest possible position to reach is #200, due to IT programming, SPSS reads this in the opposite way. This results in SPSS thinking that #200 is the highest possible position to reach and #1 the lowest position. Meaning that a positive correlation between independent variables and peak rank or debut rank will be noted as a negative number. This negative number should be read as a positive change in the chart, meaning an increase in the debut rank or peak rank of an album. In order to answer hypothesis set 2 and 3 we created two moderating variables. The first moderator, ‘LabelxNewGenre’, shows the moderating effect of genre changes of the focal product on the relationship between label size and the dependent variables. The second moderator, ‘LabelxMultigenre’, shows the moderating effect of the focal product having

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multiple genres on the relationship between the main independent variable and the dependent variables. Also these variables can be found in table 2 – 4 in the appendix.

Correlations Before testing the hypotheses, we will test for the correlations between each variable, for this the Pearson correlation coefficient is used. Preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. Here we will discuss the most important findings from the correlation analysis. The correlations between all the variables can be found in table 1 in the appendix. We found that the main independent variable ‘Album lifespan’ shows a moderate negative correlation with ‘Peak rank’ (r=-.389, p<.01 ) and with ‘Debut rank’ (r= -.297, p<.01). Meaning that according to the data set we used, when album lifespan is high, peak rank and debut rank tend to be higher as well. Besides this, ‘Album lifespan’ also shows a moderate positive correlation with ‘Label size’ (r= .32, p<.01), ‘Average number of genres per product in discography’ (r= .207, p<.05) and a moderate positive correlation with ‘Focal product is multi-genre’ (r= .260, p<.01). Meaning that when an album has a higher lifespan it is more likely that this product is released by a major label and the focal product as well as the total discography presents multiple genres. ‘Peak rank’, the second dependent variable, shows a strong positive correlation with ‘Debut rank’ (r= .924, p<.01), explaining that peak rank and debut rank move close to each other, when one goes up its very likely to say that the other goes up as well. In other words, most of our samples have the same peak rank as debut rank. Also a moderate negative correlation for ‘Peak rank’ with ‘Label size’ (r= -.250, p<.01), ‘Discography size’ (r= -.274, p<.01) and ‘Amount of previous products released by a major’ (r= -.251, p<.01) was found. Explaining that when the peak rank of an album is higher, changes are high that the album is released by a major label, has a bigger discography size and that most likely more previous albums were released by a major as well. The third dependent variable, ‘Debut rank’ shows a moderate negative correlation with the following variables: ‘Label size’ (r= -.250, p<.01), ‘Discography size’ (r= -.320, p<.01), ‘Amount of products released by major’ (r= -.294, p<.01) and ‘Genre changes within the discography’ (r= -.200, p<.05). This results from the relationship that we previously explained, namely that debut rank and peak rank move very close to each other, at a correlation of almost 1.00 (r= .924). A correlation among the control variables worth mentioning is the moderate positive correlation between ‘Amount of previous products

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released by major’ and ‘Genre changes within discography’, (r= .313, p<.05). Showing that when more products are released by a major, more genre changes occur.

Regressions Figure 3: “The effect on album lifespan.”

Genre change Peak rank

Label size Album lifespan

Previous Debut rank performance (control)

The first four models of our regression analysis show the relation between label size and genre change and how these variables have an effect on the album lifespan of an album in the Billboard Top 200 Chart. Used to answer the first set of hypotheses.

H1a states that when an album is released through a major label the lifespan of this album will be longer compared to an album released through an independent label. Support was found for this hypothesis, since we found a positive correlation between the two variables. Also the regression analysis in model 2 showed, with an explained variance of 14% that when an album is released by a major label it will approximately have a lifespan that is 15 weeks longer than an album released by an independent label (B= 15.193, SE= 4.943, p<.01). This shows that when it is assumed that the control variables and the independent variables we set are the only factors influencing the sales of an album as a product, the release by a major label will have higher sales than the release by an independent label.

Hypothesis H2a stated that genre change in the focal product would have a positive moderating role on the relationship between label size and album lifespan.

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Therefore we expect albums to have a higher album lifespan when the moderator is included in the regression model. Model 4 shows that after including the moderator as a variable; the coefficient of label size is increasing. Meaning that album lifespan will be longer. However, we did not find any significance for the moderating variable and therefore cannot support H2a.

H3a predicted that multi-genre products would have a positive moderating role on the relationship between label size and album lifespan. Meaning that when an album is released and it presents multiple genre types, it will stimulate the relationship between label size and album lifespan and therefore sell more albums. We found a decrease in the coefficient of label size, explaining that album lifespan would decrease as a result of the moderating variable. Unfortunately, also this moderating variable did not show any significance. The effect of the control variables during this first set of regressions with album lifespan as the dependent variable was small. Significance was only found for ‘Amount of previous products that are albums’ (B= -.828, SE= .494, p<.1) and for ‘Average number of genres per product in discography’ (B= 10.299, SE= .5717, p<.1). These results are presented in model 2. Although significance was found, this was so little that the effect of these control variables is questionable.

Figure 4: “The effect on peak rank.”

Genre change Peak rank

Label size Album lifespan

Previous Debut rank performance - (control)

The second four models show the relation between label size and genre change and how these variables have an effect on the peak rank of an album in the Billboard Top 200 Chart. In model 6 from table 3 and the correlation table, it shows with an explained variance of 19% that there is a significant relationship between peak rank and label size (B= -34.100, SE= 10.577, p<.01). Meaning that when an album is released by a major label instead of an independent label, the peak rank will be on average 34 places higher. These results support

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H1b, which stated that products released by a major record label would have a higher peak rank than products released by an independent record label.

H2b stated that genre change in the focal product would have a positive moderating role on the relationship between label size and peak rank. The result that was found, as presented in model 8, shows with an explained variance of 22%, that a significant moderating effect appears when ‘genre changes in the focal product’ is included as a moderator in the regression. Leading to a stronger relationship between label size and peak rank and therefore supporting H2b.

H3b formulates the following prediction, multi-genre products will have a positive moderating role on the relationship between label size and peak rank. According to the regression analysis, no significant moderation effect was found to support the hypothesis. In this second set of regressions, explaining the effect on peak rank, we found significance results for the control variable “Discography size” in all of the models. Model 6 shows (B= -1.101, SE= .569, p< .1), model 7 shows (B= -1.091, SE= .577, p< .1), model 12 shows (B= -1.104, SE= .582, p< .1 & B= -1.183, SE= .594, p< .05). Explaining that discography size does influence album success. However for most models the significance level was very small and therefore is the influence of discography size in these models questionable.

Figure 5: “The effect on debut rank.”

Genre change Peak rank

Label size Album lifespan

Previous Debut rank performance (control)

The third and final set of four models show the relation between label size and genre change and how these variables have an effect on the debut rank of an album in the Billboard Top 200 Chart.

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Model 10 shows, with an explained variance of 18%, that the debut rank will increase with 26 places in the chart when an album is released by a major label instead of an independent label

(B= -26.050, SE= 11.100, p<.05). Supporting H1c, which stated that a major label release would result in a higher debut rank. To show the moderating effect of genre changes in the focal product as presented by

H2c we look at the findings from model 12. Showing, with an explained variance of 23%, that after adding the moderator to the regression model, the coefficient of label size significantly increases, leading to a higher peak rank. Since the moderating effect is significant, we support the hypothesis.

H3c states that when the focal product shows multiple genres it will benefit the relationship between label size and debut rank. Due to insignificance in the regression analysis we found no support for this hypothesis. In the final set of regressions the control variables showed most significance. ‘Discography size’ showed the following effect: model 10 (B= -1.315, SE= .598, p< .05), model 11 (B= -1.254, SE= .603, p< .05) and model 12 (B= -1.257, SE= .604, p< .05 & B= - 1.360, SE= .623, p< .05). The effect seems to be constant during the different regressions leading to a positive significant effect, when discography size is higher, debut rank of an album will increase. Besides ‘Discography size’, also ‘Amount of previous products released by a major’ shows a significant effect in the following models: model 10 (B= -1.354, SE= .751, p< .1), model 11 (B= -1.341, SE= .754, p< .1) and model 12 (B= -1.336, SE= .760, p< .1). Meaning that the fact that previous products of an artist were released by a major has a positive effect on the debut rank of the latest release, whether this new product is released by a major or not. Since the significance level is very low, further research in this topic is suggested.

Discussion Previous literature argues that the influence by a major label on the success of a product has fallen (Bhattacharjee, 2007, p.1368). A release by a major label seems what every artist wants, power in distribution, marketing and retail. There are examples of artists making it big through an independent label, without any help of a major label. Although there are a few artists that reach superstar status this way, it happens on a rare occasion. The fact that some artists sell more than others cannot simply be explained by the fact that one artist releases through a major label and the other artist releases through an independent label (Franck & Neusch, 2012, p.202). It depends on multiple external factors that we cannot control for, such

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as talent, unforeseen situation during the time of release. Some might say the more talent and the higher the quality of the music the more sales an album will make (Rosen, 1981), unfortunately are talent and quality variable that are impossible to control for. Since people have different perceptions of the term quality and different interests (Franck & Neusch, 2012, p. 205). Besides this, previous literature argues if talent is even necessary to have successful sales in the music industry (Adler, 1985). Therefore in order to make this research as significant as possible and control for as many accepted variables as possible, we disregarded talent and quality and focussed to determine on the difference between major label sales and independent label sales. With as main goal to answer the following question: What is the effect of label size upon the chart lifespan of an album? In order to do so, we set up twelve different regression models to find quantitative results to this subject. First of all, our first set of regressions shows results with which we are able to answer the first set of hypotheses. Namely, does a release by a major label result in higher sales in terms of album lifespan, peak rank and debut rank, compared to a release by an independent album? We found that when a major label releases an album, the album is likely to have better performances compared to an album that is released by an independent label, resulting in higher sales. Leading to supporting the first set of the hypotheses. These findings are consistent with previous research that showed that albums promoted by major labels tend to survive longer in the charts than albums promoted by an independent label (Bhattacharjee et al., 2007, p.1368). During their research Bhattacharjee et al. showed that independent labels were closing the gap with the major labels. However this goes beyond the content of a bachelor thesis and is not shown during this research. Since the charts during their research are different than the charts used for this research, we cannot compare data in order to make such an assumption. Additionally to these findings we found that a release by a major record label also results in an higher debut rank and since debut rank and peak rank have a correlation close to 1.00, it is also given that a major release will most definitely result in a higher peak rank. Besides the effect that label size has on these three dependent variables it was also researched how genre changes of the focal product and whether or not the focal product presents multiple genres, influences the relationship between label size and the chart performance of an album. For the first moderator we used, ‘Genre change in the focal product’, we found a significant effect to support H2b and H2c. Meaning that when an album is released by a major the positive effect that a major label has on peak rank and debut rank, is

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stimulated when the focal product shows a completely new genre. A higher peak rank could lead to reaching more potential customers. Although the effect does not show in the album lifespan it might lead to more single purchases or an increase in ticket sale for a show, certainly it creates more awareness of the album. ‘Multiple genres were presented by the focal product’ was used as the second moderator between the relationship of label size and album chart performance. Based on the regressions performed and presented in model 4, 8 and 12, we found no significance for any of the dependent variables. Meaning that when a product is released the amount of genres that is presented does not affect the successfulness of sales. Finally, the control variables showed that when the discography size of an artist is bigger, this could result in a higher peak and debut rank. An explanation for this could be that the artist is more known and has a bigger fan base than artist with a smaller discography size. Since it are the ‘committed’ fans that are responsible for the debut rank of an album, because they most likely will buy the album on the day of the release. These findings are in line with previous research conducted by Bhattacharjee et al (2007, p. 1361). Another result on previous performance by an artist that was found is that when more previous products are released by a major the debut rank of the new released album will most likely be higher. An explanation for this could be that the promotion and distribution power of a major label will reach more potential fans than a smaller independent label. Creating a bigger fan base, leading back to the explanation by Bhattacharjee et al. that the debut rank is the result of purchases by the ‘committed fan base’ (2007, p. 1361).

Further work is required to test the generalizability of our results. For future research we suggest that streaming, online sales and different charts are taken into consideration to create a generalizable answer to our research question. Streaming services such as ‘Spotify’ and ‘Deezer’ are becoming more and more popular and therefore also more important to label managers and artists (Caroline Benelux). This results in a change in how the music market is set up and how labels should market their products. The physical album sales are diminishing and this market share can now be found in the online streaming services, setting a new standard to music sales, as we know it (Caroline Benelux). Besides the online streaming services it would be interesting to see how online music stores such as ITunes influence the relationship between label size and album lifespan. However the information needed to proceed with this research is extremely hard to obtain, since this information is considered confidential.

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To research how the effect of major labels on album sales has fallen it would be interesting to see how the influences of major labels differ compared to ten years ago. With the rise of social media and the above-mentioned online businesses we consider the influence of independent labels on album sales higher now than before the social media era (Bhattaacharjee et al., 2007, p. 1372). Unfortunately this type of research goes beyond the level of a bachelor thesis and is therefore proposed for future research. It is important during this future research to consider the differences per country, since the popularity of streaming is not the same in every country. In Sweden for example, streaming services like Spotify take over around 65% of the market, in The Netherlands this is around 25 % (Caroline Benelux). Finally, we would like to suggest the use of different charts to compare the superstar sales with sales in the niche markets. To get a broader view of how the influences of label size are spread across the music industry.

Conclusion During this research and the internship at Caroline Benelux it became clear that there are a lot of external factors influencing the sales of an album. The clearest result that was found during this research and the answer to the question: “What is the effect of label size upon the chart lifespan of an album?” Is that when an album is released through a major label, the album sales will be higher. The music industry is still dominated by the three major record labels that use the super star effect and big marketing budgets to promote new album releases. As an answer to the sub-question: “How does genre changes moderate the relationship between label size and album lifespan?” We found that genre changes do not affect the lifespan of an album. Although results showed that genre changes do have a supporting effect on the relationship between label size and peak / debut rank. We would like to propose the following recommendations for label managers of independent labels. Supported by previous research, a ‘committed fan base’ will result in higher ranks in the chart creating more awareness for an album. It is therefore of importance for label managers that they try to create a buzz around the release of an album, try to create this ‘committed fan base’. By using for example TV appearances or collaborations with artists with a bigger fan base. In order to use these mechanisms, connections are very important, whether it is radio, TV, or a page in a magazine, more and stronger connections will help your album releases to perform better in the charts.

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Bibliography Adler, M. (1985). Stardom and talent. The American Economic Review, 208-212.

Bhattacharjee, S., Gopal, R. D., Lertwachara, K., Marsden, J. R., & Telang, R. (2007). The effect of digital sharing technologies on music markets: A survival analysis of albums on ranking charts. Management Science, 53(9), 1359-1374.

Bradlow, E. T., P. S. Fader. 2001. A Bayesian lifetime model for the “Hot 100” billboard songs. J. Amer. Statist. Assoc. 96 368–381.

Caroline Benelux, subsidiary company of Universal Music Group.

Connolly, M., and A. B. Krueger. “Rockonomics: The Economics of Popular Music,” in Handbook of Economics of Art and Culture, edited by V. Ginsburgh and D. Throsby. Amsterdam, The Netherlands: Elsevier, 2006, 667–719.

Elberse, A. (2007). The power of stars: Do star actors drive the success of movies?. Journal of Marketing, 71(4), 102-120.

Franck, E., & Nüesch, S. (2012). Talent and/or Popularity: What does it take to be a Superstar?. Economic Inquiry, 50(1), 202-216.

Helmink, F. (2014, March 30) Succes artiesten meer op verschillende manieren getoetst. Retrieved from www.nu.nl

OLS limitations. Retrieved from http://www.southalabama.edu/coe/bset/johnson/oh_master/Ch14/Tab14-01.pdf

Rosen, S. (1981). The economics of superstars. The American economic review, 845-858.

Rovi company information. Retrieved from www.rovicorp.com/company

Ugrinowitsch, C., Fellingham, G. W., & Ricard, M. D. (2004). Limitations of ordinary least squares models in analyzing repeated measures data. Medicine and science in sports and exercise, 36, 2144-2148.

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Appendix

Table 1. Correlations

Mean Std. Dev. Min. Max. 1 2 3 4

1. Album lifespan 14.44 26.34 1 150 1.00 62.21 57.98 1 196 -.389** 1.00 2. Peak rank 3. Debut rank 67.03 60.53 1 196 -.297** .924** 1.00 4. Label size .66 .48 0 1 .320** -.322** -.250** 1.00 5. Focal product presents completely new genre .12 .32 0 1 .015** -.002 .075 .100 6. Focal product is multi-genre .25 .44 0 1 .260** -.097 -.092 .136** 7. Discography size 11.93 13.25 0 70 -.003 -.274** -.320** .058 8. Amount of previous products released by major 7.86 10.66 0 63 .000 -.251** -.294** .070 9. Amount of previours products that are albums 6.09 7.60 0 49 -.103 -.135 -.175 .004 10. Genre changes within discography 1.04 1.54 0 9 .078 -.178 -.200* .227 11. Average number of genres per product in 1.00 discography 1.27 .42 2.87 .207* -.162 -.153 .214*

+ Significance: ***p< .001,**p< .01,*p< .05, p< .1 N = 119

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Table 1. Correlations (continued)

5 6 7 8 9 10 11

1.00 .449 1.00 -.048** .147 1.00 -.034** .059 .671** 1.00 -.022 .070 .715 .730 1.00 -.044 .186 .477 .313* .407 1.00 .135 .544* .229 .070 .141* .547* 1.00

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Table 2. Regression results

Model 1 Model 2

Dependent variable Album lifespan Album lifespan Coefficient SE Beta Coefficient SE Beta

Control variables - Discography size .069 .284 .034 .065 .266 .033 - Amount of previous products released by major .406 .346 .164 .299 .334 .121 + + - Amount of previous products that are albums -.968 .516 -.279 -.828 .494 -.239 - Genre changes within discography -.010 2.068 -.001 - Average number of genres per product in + discography 14.117* 6.779 .227 10.299 .5717 .166 Label size - Focal product released by major label 15.193** 4.943 .275 Genre change - Focal product presents completely new genre - Focal product is multi-genre or not Moderator - LabelxNewGenre - LabelxMultigenre R² .076 .147 + Significance: ***p< .001,**p< .01,*p< .05, p< .1 N = 118

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Table 2. Regression results (continued)

Model 3 Model 4 (moderating test)

Album lifespan Album lifespan Album lifespan Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta

.011 .263 .005 .086 .276 .043 .031 .272 .016 .271 .329 .110 .297 .338 .120 .295 .331 .119 -.746 .487 -.215 -.783 .505 -.226 -.758 .494 -.219

+ 2.480 6.639 .040 12.944 .6777 .208 5.201 7.699 .084

15.556** 4.878 .282 16.772** 5.315 .304 11.767* 5.523 .213

-11.466 7.947 -.141 4.630 15.083 .057 16.342* 6.933 .271 -.835 11.021 -.014

-10.637 17.337 -.117 16.495 11.969 .248 .189 .153 .188

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Table 3. Regression results

Model 5 Model 6

Dependent variable Peak rank Peak rank Coefficient SE Beta Coefficient SE Beta

Control variables + + - Discography size -1.082 .611 -.247 -1.101 .569 -.252 + - Amount of previous products released by major -1.388 .744 -.255 -1.146 .716 -.211 + - Amount of previous products that are albums 1.932 1.109 .253 1.591 1.057 .209 - Genre changes within discography -.835 4.441 -.022 - Average number of genres per product in discography -15.123 14.560 -.111 -8.002 12.234 -.059 Label size - Focal product released by major label -34.100** 10.577 -.281 Genre change - Focal product presents completely new genre - Focal product is multi-genre or not Moderator

- LabelxNewGenre

- LabelxMultigenre

R² .120 .194 + Significance: ***p< .001,**p< .01,*p< .05, p< .1 N = 118

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Table 3. Regression results (continued)

Model 7 Model 8 (moderating test)

Peak rank Peak rank Peak rank Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta

+ + -1.091 .577 -.249 -1.104 .582 -.252 -1.183* .594 -.270 -1.146 .722 -.211 -1.164 .712 -.214 -1.122 .724 -.206 1.585 1.069 .208 1.539 1.063 .202 1.567 1.079 .205

-8.409 14.566 -.062 -14.382 14.276 -.105 -10.489 16.821 -.077

-34.288** 10.701 -.282 -41.713* 11.195 -.343 -39.130** 12.067 -.322

3.585 17.436 .020 -52.637 31.771 -.294 .053 15.209 .000 -14.016 24.080 -.105

74.930* 36.518 .376

21.704 26.151 .148

.194 .224 .199

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Table 4. Regression results

Model 9 Model 10

Dependent variable Debut rank Debut rank Coefficient SE Beta Coefficient SE Beta

Control variables - Discography size -1.276* .628 -.279 -1.315* .598 -.288 + - Amount of previous products released by major -1.542* .765 -.272 -1.354 .751 -.239 + - Amount of previous products that are albums 1.994 1.140 .250 1.706 1.109 .214 - Genre changes within discography -1.463 4.566 -.037 - Average number of genres per product in discography -12.187 14.971 -.085 -8.140 12.840 -.057 Label size - Focal product released by major label -26.050* 11.100 -.205 Genre change - Focal product presents completely new genre - Focal product is multi-genre or not Moderator

- LabelxNewGenre

- LabelxMultigenre

R² .146 .185 + Significance: ***p< .001,**p< .01,*p< .05, p< .1 N = 118

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Table 4. Regression results (continued)

Model 11 Model 12 (moderating test)

Debut rank Debut rank Debut rank Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta

-1.254* .603 -.274 -1.257* .604 -2.75 -1.360* .623 -.298 + + -1.341 .754 -.236 -1.380 .739 -.243 -1.336 .760 -.235 1.648 1.116 .207 1.659 1.104 .208 1.724 1.134 .216

-6.788 15.208 -.048 -15.530 14.824 -.109 -8.454 17.668 -.059

-26.999* 11.173 -.213 -35.492** 11.625 -.280 -30.596* 12.674 -.241

19.891 18.205 .106 -51.908 32.992 -.277 -6.243 15.880 -.045 -14.466 25.292 -.104

90.644* 37.921 .436

21.622 27.468 .142

.194 .233 .190

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Major labels Based on a list on Wikipedia we linked back all the labels that these major labels own or have a joint share in and treated them as a ‘major label’ - Universal Music Group: - Sony Music Entertainment - Warner Music Group - EMI (before dismantling in 2013)

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