University of Amsterdam Faculty of Business and Economics

MSc Business Administration Entrepreneurship and Management in the Creative Industries track

Genre Spanning and Audience Appeal as Antecedents of Genre Consensus: The Case of Electronic Dance Music DJs

Master Thesis June 29, 2015

Student: Valerie Bollen 10837949

First Supervisor: Bram Kuijken

Statement of Originality

This document is written by student Valerie Bollen 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.

1

Table of Contents

I. Abstract 4 II. Acknowledgements 5 III. List of Tables and Figures 6

1. Introduction 7, 8

2. Literature Review 9 2.1 An Introduction to Genre/Category Theory 9 – 11 2.2 Genre Consensus 11 – 13 2.3 Genre Spanning and Audience Appeal 14 – 16 2.4 Word-of-Mouth Theory: Creating the ‘Buzz’ 16 – 18 2.5 Hypotheses 18 – 20

3. Method 21 3.1 Sample 21 3.2 Data Collection 22 3.2.1 DJ Databases 22 – 24 3.2.2 Social Media & Ranking Lists 24 – 26 3.3 Genre Classification System Development 27 – 30 3.4 Variables and Measures 31 – 34

4. Results 35 4.1 Descriptive Statistics 35 – 42 4.2 Regression Analyses 43 4.2.1 Genre Spanning and Genre Consensus 43, 44 4.2.2 Audience Appeal and Genre Consensus 44 4.3 Robustness Checks 45

2

5. Discussion 46 5.1 Main Findings 46, 47 5.2 Implications 47, 48 5.3 Limitations 48, 49 5.4 Suggestions for Future Research 49, 50

6. Conclusion 51

References 52 – 58

Appendices A. Sampling Frame DJ Mag Top 100 DJs 2010 – 2014 59 – 62 B. Sample DJ Mag Top 100 A-Z 63, 64 C. Original Genre Classification Systems 65

3

I. Abstract

In the music industry, genre categorization systems play an important role in audiences’ evaluation of artists. If audiences are in agreement about an artist’s category-membership, this may positively affect the artist’s career. However, little research has addressed the predictors of genre consensus. Therefore, this study investigated the antecedents of genre consensus by seeking to answer the question: “To what extent function genre spanning and audience appeal as the antecedents of genre consensus?”

The Electronic Dance Music (EDM) industry was selected as empirical setting since it is a largely neglected industry in scientific literature and because genres have a tremendous influence on the EDM community. The study focused on the 187 most prominent EDM DJs who had obtained a position in the DJ Mag Top 100 list in the period of 2010 – 2014.

Drawing on data from four major online DJ databases, several social media websites and commercial music platforms, it was examined whether genre spanning and audience appeal influenced genre consensus.

The results suggested that genre spanning negatively affected genre consensus, while repeated appearances on the DJ Mag Top 100 list had a positive effect. Contrary to my expectations, the artists´ years of experience in the music industry did not moderate the negative effect between genre spanning and genre consensus.

This study contributes to theories of categorization and organizational ecology by identifying the effects of genre consensus and audience appeal on genre consensus, and provides some important insights for artists, labels and other managers in the EDM industry.

4

II. Acknowledgements

First and foremost I offer my sincerest gratitude to my supervisor Bram Kuijken MSc – thank you very much for your guidance, encouragement and valuable feedback throughout this research project.

Secondly, I would like to show my greatest appreciation to prof. dr. N. M. Wijnberg for his illuminating insights that helped solving the puzzle of the rationale in this thesis.

Last but not least, I would like to express my appreciation towards my family and friends who have supported me throughout the entire process.

Valerie Bollen

5

III. List of Tables and Figures

Tables

Table 1 – Changes in Genre Classification Systems Based on Name Similarity 28

Table 2 – Genre Representation Across the Four Sources 29

Table 3 – Changes in Genre Classification Systems Based on Subgenre Identification 30

Table 4 – DJ Avicii Example Jaccard Similarity Coefficient Calculation 34

Table 5 – Genre Frequencies and Percentages per Source 36

Table 6 – Times Ranked in DJ Mag Top 100 List 2010 – 2014 39

Table 7 – Pairwise Consensus Comparisons Between Sources 40

Table 8 – Mean, Standard Deviation and Correlations of Study Variables 42

Table 9 – Linear Regression Analysis Genre Spanning and Genre Consensus 43

Table 10 – Moderation Model of Predictors of Genre Consensus 44

Table 11 – Linear Regression Analysis Times Ranked and Genre Consensus 44

Table 12 – Linear Model of the Predictors of Change in Genre Consensus Scores 45

Figures

Figure 1 – Histogram: number of DJs per country 35

Figure 2 – Histogram: genre spanning 37

Figure 3 – Box plot: years of experience 38

Figure 4 – Box plot: genre consensus 40

6

1. Introduction

Nowadays, consumers make use of online platforms to discover, discuss and rate music, share personal playlists and vote for their favourite artists. As a result, it has become more important for artists to construct and manage an online identity (Koosel, 2013), and genres help to facilitate this identity construction (Shocker, Bayus, & Kim, 2004). Consequently, genre categorization systems are more important than ever because they capture the way music is divided in the minds of consumers, and the way the production and distribution of music is structured (DiMaggio, 1987). These categorization systems have an impact on how individuals shape their music tastes and make sense of different artists and their identities

(Mattsson, Peltoniemi, & Parvinen, 2010), which influences their behaviour and consumption patterns and thereby influences the chances of artists’ success (Zuckerman & Kim, 2003).

Genre categorization systems differ across societies and among their members

(DiMaggio, 1987). Even though audiences and producers collectively shape the structure of these systems through interaction, they may still differ in how they apply category labels

(Rosa, Porac, Runser-Spanjol, & Saxon, 1999). In addition, an artist may even be associated with different genres across audiences. However, if audiences are in agreement regarding the genre profile of an artist, this is called genre consensus.

Some argue that genre consensus should be strived for because of the positive effect on audience appeal (Hsu, 2006). That is, whether an offering is intrinsically appealing to the members of the audience (Hannan, 2010). A lack of genre consensus may be a barrier to the legitimation of an artist’s genre profile (Baumann, 2007), which may lead to devaluations from the audience members (Zuckerman, 1999) and thereby negatively affect artist success.

Interestingly, little research has examined the determinants of genre consensus. Prior research has found that consensus is influenced by the degree of connectivity between audience members and organizations, the degree of interactions, and audience member

7 turnover (Cattani, Ferriani, Negro, & Perretti, 2008). Also, it has been clarified how consensus can be reached among the members of a cultural community through legitimation and justification (Baumann, 2007), and how this leads to an increase in popularity and appeal

(Scott, 2012). Moreover, the effects of category spanning and category consensus on audience appeal have been explored (Hsu, 2006).

However, to my best knowledge, genre spanning and audience appeal have not yet been measured in the literature as antecedents of genre consensus, even though this may provide some important insights for artists, and music marketing strategists. Therefore, this study seeks to answer the following research question:

“To what extent function genre spanning and audience appeal as the antecedents of genre consensus?”

The empirical focus is on Electronic Dance Music (EDM) industry in which disk-jockeys

(DJs) represent the most prominent artist type. This industry is not only largely neglected in scientific literature, but also one in which genres have a tremendous influence (McLeod,

2001). The sample consists of 187 EDM DJs who obtained a position in the DJ Mag Top 100 list in the period of 2010 – 2014. Secondary data from numerous online databases and platforms are assessed, among which are DJ Mag, DJ Rankings, Partyflock, The DJ List, and

Top Deejays. These databases are used as they attract millions of visitors and make use of genre categorization systems to classify artists.

The objective of this study is two-fold. First, it strives to contribute to categorization theory and organizational ecology theory by identifying the effects of genre spanning and audience appeal on genre consensus. Second, it seeks to offer insights for artists, labels, and booking agencies by addressing the implications of the usage of genres.

8

2. Literature Review

“Art worlds typically devote considerable attention to trying to decide what is and isn’t art, what is and isn’t their kind of art, and who is and isn’t an artist” (Becker, 1982, p. 36).

2.1 An Introduction to Genre/Category Theory

Following DiMaggio (1987), genres are defined as “sets of artworks classified together on the basis of perceived similarities” (p. 441) and represent socially constructed categories. They are used to classify varieties of cultural products, particularly in the fields of visual art, popular culture, film, literature, and music (Lena & Peterson, 2008). Within these fields, categorization systems shape organizational dynamics and success (Hirsch, 1972). DiMaggio

(1987) explained that these so-called social artistic classification systems (ACSs) capture two sets of processes: the way art is divided in the minds of consumers and the way institutions structure the production and distribution of art. ACSs vary along four dimensions; (1) differentiation, (2) hierarchy, (3) universality and (4) boundary strength. More specifically, they differ in (1) the number of institutionally bounded genres, (2) the degree to which genres are hierarchically ranked by prestige, (3) the degree to which classifications are similar among subgroups of members, and (4) the degree to which tastes are clustered within ritual boundaries.

If boundary strength is low, a category’s boundaries are called ‘fuzzy’ (Hannan,

2010). Fuzziness arises (1) when there is disagreement among producers and audiences about which attributes and behaviour is typical for the category, and (2) when category members have memberships in multiple categories (Vergne & Wry, 2014). The offerings of an artist or organization must possess specific category attributes in order to fall within a category’s

9 boundaries, which may be challenging because boundaries change over time and differ across audiences (Vergne & Wry, 2014). In addition, Lena and Peterson (2008) mentioned that genres are constantly debated in dialogues among fans, artists, critics and marketing strategists, which provides the opportunity for contesting musical quality and social prestige.

Due to these interactions, genres emerge, evolve and disappear over time (Lamont & Molnár,

2002). It also leads to the emergence of subgenres; subordinate categories within a particular genre (McLeod, 2001). This boundary work is crucial because genres often compete for the same resources such as fans, capital, media attention, and legitimacy (Lena & Peterson,

2008).

In their review of categorization literature, Vergne and Wry (2014) distinguished between two types of organizational categorization theory: self-categorization and categorical imperative. The self-categorization perspective, or cognitive psychological approach, emphasizes how strategic managers perceive the external environment and their firm’s position within that environment, and focuses on aspects such as power, politics, interest seeking, and strategic co-optation (Porac, Wade, & Pollock, 1999). According to this producer point-of-view, strategists construct mental models of the competitive environment, and in turn these managerial perceptions determine the structure of the industry (Porac,

Thomas, & Baden‐Fuller, 1989). In this case, producers pursue self-selection into a category through imitative behaviour and strategic use of linguistic tools. Therefore, category labels are crucial as they help producers seek membership in an existing category (Vergne & Wry,

2014).

In contrast, when adopting the categorical imperative perspective, categories are described from the audience-side. Audiences are homogeneous set of agents who have an interest in a specific field and control over material and symbolic resources which affect the success of organizational actors in that field (Hsu & Hannan, 2005). The core idea of the

10 categorical imperative is that audience members (e.g. critics, media, and consumers) attach labels to categories, with which they associate a set of properties and rules that category members (e.g. organizations or artists) are expected to follow. As a result, the labels facilitate an evaluation process in which audience members determine which category an organization or artist fits into and whether this matches their expectations (Vergne & Wry, 2014).

Therefore, categorization simplifies information processing and decision making for potential consumers as it provides a context in which similarities and differences among cultural products and producers can be highlighted (Shocker et al., 2004).

Hsu (2006) emphasized that an organization should ensure an intrinsic fit between its offerings and the audience’s taste preferences, and devote some level of engagement to make its offerings available to potential audience members. Without this, organizations are not able to garner resources from them. This can be illustrated by an example from the music industry.

Audience members compare the attributes of artists that enter the music industry to a collective system of categories and social codes (Mattsson et al., 2010). For instance, if an artist´s music is profiled as a house music, and its attributes match with the expected category characteristics and social codes, the artist will be perceived as a legitimate member of the house genre. However, if audience members perceive a deviation in attributes, this may lead to devaluations (Zuckerman, 1999). In that case, people may reject artists, which is likely to negatively influence their artistic careers.

2.2 Genre Consensus

In cultural industries, an agreement among audiences regarding the genre(s) of an artist is referred to as genre consensus. Put differently, there is genre consensus when there is a perceived fit of category-membership among audiences (Hsu, 2006).

11

In general, consensus is issue-specific, which implies that any specific norm, belief or practice is more or less consensual in a situation (Zelditch, 2001). Adopting the self- categorization perspective, there is category consensus if organizations with common attributes perceive themselves as members of a particular category (Vergne & Wry, 2014).

However, this study will assess consensus following other researchers, who have drawn upon the categorical imperative, or ecological perspective.

From an ecological point-of-view, consensus among audiences is reached once the features and activities of producers have become taken-for-granted elements (Cattani et al.,

2008). According to Zelditch (2001), this is achieved through justification, which is “an argument made to explain how the unaccepted is in fact acceptable because it conforms to existing, valid norms, values, or rules” (Baumann, 2007, p. 49). From the field of sociology it can be learned that, once the elements of a social order are justified and seen as in agreement with norms, values, and beliefs that individuals presume are widely shared, legitimation occurs (Weber, 1978). Put simply, legitimation refers to the process of how categories or category members gain and maintain acceptance (Vergne & Wry, 2014). Delegitimation therefore refers to the process of losing this acceptance (Berger, Ridgeway, Fisek, & Norman,

1998). According to Hannan, Pólos, and Carroll (2007), legitimation from the ecological perspective is viewed as “conformity of feature values to schemata,” in the sense that it

“grows with the level of consensus within the audience about the meaning of a label’ (p. 98).

A genre only exists if it is recognized as a salient unit of analysis by a sufficient number of members (e.g. artists) and audiences (e.g., critics, media, and consumers) (Vergne

& Wry, 2014). Ridgeway and Correll (2006) drew upon status construction theory in order to explain how encounters between people within a social community spread status beliefs as one teaches a previously acquired belief to another. Beliefs and knowledge of genres is derived from interaction and participation in a cultural community (Berkenkotter & Huckin,

12

1995). Therefore it is reasoned that dialogues within such communities facilitate justification for status beliefs regarding the characteristics and identities of producers and artists.

Cattani et al. (2008) contributed to this rationale as they discovered that consensus is affected by the network structure because this facilitates interaction between audiences and organizations. They specified that audience members have an influence on the organizations’ survival as they reach, reinforce, and preserve consensus about the organizations’ features and behaviour. Furthermore, they suggested two conditions which account for the creation of consensus among audiences about which characteristics and attributes firms must show in order to be accepted or excluded from a particular category. They identified (1) the degree of connectivity of the network between audiences and organizations, and (2) the degree of repeated interactions, as antecedents of consensus. In addition, they found that audience turnover destabilizes consensus (Cattani et al., 2008).

Other research in the field of organizational ecology has focused on genre consensus as a predictor rather than an outcome. For instance, Hsu (2006) highlighted that when offerings clearly fall within a certain category and establish a clear fit with the targeted taste position, this allows the audience to understand the characteristics that these offerings have in common, which has a positive effect on audience appeal.

In this study, genre consensus is considered to be a desired outcome as it is reasoned to legitimate art forms, and contribute to appeal and an artist’s survival within the field.

However, it must be mentioned that consensus will never be absolute, as there is never complete consensus within a society about anything (Baumann, 2007). As a result, consensus at the collective level – and not necessarily at the individual level – is considered to be sufficient to reach legitimation (Baumann, 2007; Zelditch, 2001).

13

2.3 Genre Spanning and Audience Appeal

An organization engages in category spanning when it has simultaneous membership in two or more categories (Vergne & Wry, 2014). Correspondingly, an artist engages in genre spanning through membership in two or more genres at the same time. The concept of niche is relevant here, which refers to a small group of customers with similar needs and preferences (Dalgic & Leeuw, 1994). In niche marketing, a company focuses on a certain category to fulfil those customers’ needs. Spanning multiple categories indicates a wider niche width, which is expected to attract a broader audience (Hsu, 2006). But whether an offering is intrinsically appealing to the members of the audience (that is, whether audience appeal is high) depends on how well the offering matches with the taste of that audience

(Hannan, 2010).

Niche width measures the range of environmental dimensions across which an organization exists (Carroll, 1985). Taking the newspaper publishing industry as illustration,

Carroll (1985) explained the divide between generalist and specialist firms. While the former are organizations that seek to exploit a wide array of sources, the latter focus on only one or a limited few domains. When addressing the topic of category spanning, researchers often make this division between generalists and specialists (Hsu, 2006; Zuckerman, Kim, Ukanwa, & von Rittmann, 2003), in the sense that generalists engage in category spanning, while specialists do not.

From the self-categorization perspective, it is argued that spanning categories may foster organizational success through competitive differentiation (Porac et al., 1989). In some cases, strategic manipulation of multiple identities can be a source of power (Padgett &

Ansell, 1993). Moreover, categories with many subcategories may allow their members more leeway to innovate than categories with few sub-categories (Brewer, 1993). However, according to Zuckerman et al. (2003), the difficulty of spanning lies in successfully occupying

14 several roles with respect to the same audience, and this often outweighs its advantages.

Prior research which adopted the categorical imperative point-of-view has addressed that organizational identities are built around codes and rules which audiences regard as standards for a producer or firm (Hsu & Hannan, 2005). So, membership in categories constitutes part of the identities of producers and organizations, and these identities may or may not appeal to the members of the audience. McKendrick, Jaffee, Carroll, and Khessina

(2003) discovered that producers who seek membership in multiple market categories are less likely to construct clear and appealing identities to relevant audiences. Besides lower appeal, category spanning was also found to result in less legitimation (Zuckerman, 1999). On the other hand, focused or specialized identities have an advantage because they facilitate valuation and legitimation (Zuckerman et al., 2003).

According to Negro, Hannan, and Rao (2010), category spanning may lower audience appeal because of: (1) partiality of category memberships (atypicality), (2) categorical contrast, and (3) expertise or capability. Regarding atypicality, it is argued that category spanning may lower the appeal of offerings in categories because it confuses audiences (Hsu,

2006). As mentioned before, when offerings clearly fall within a certain category, this allows the audience to understand the characteristics which these offerings have in common.

Consequently, if the offerings are too complex to match with audience members’ expectations and perceptions of a particular category, they will have more difficulty interpreting the identity of the producer or organization, and such blurred identities lead to confusion and lower appeal (Hsu, 2006).

Second, spanning categories is also found to negatively influence appeal through lowered categorical contrast – that is, a decrease in the sharpness of a category’s boundaries.

This leads to a growing disagreement about the category and thereby reduces appeal (Negro et al., 2010).

15

Third, category spanning has been theorized as a potential influencing factor on the perception of expertise or capability, in the sense that producers or organizations who span multiple categories are expected to develop less expertise in each category in comparison to category specialists, which would result into a lower fit with the category schemas and subsequently lower audience appeal (Hsu, Hannan, & Koçak, 2009). Even actors who manage to develop a high level of expertise in multiple categories face the difficulty of convincing audiences of this (Kovács, Hannan, & Sorenson, 2015). However, Hsu et al. (2009) found empirical evidence that generalists were not devalued because spanning categories indicated poor skill, but because they were not perceived as genuine full members of a particular category.

On the contrary, an advantage of generalists is that spanning facilitates flexibility

(Zuckerman et al., 2003). They attract larger audiences, and spreading risk across multiple regions of the environment may help them to outlast specialists (Hsu, 2006). On top of that,

Pinheiro and Dowd (2009) found that aesthetic generalism (that is, being conversant in multiple genres), had a positive effect on the earnings and national recognition of jazz musicians. Finally, Hsu (2006) hypothesized and found that genre consensus mediates the negative effects of genre spanning on audience appeal. But, in order to reach genre consensus, interaction (Cattani et al., 2008), justification, and legitimation is needed (Baumann, 2007).

This is where word-of-mouth communication comes into play.

2.4 Word-of-Mouth Theory: Creating the ‘Buzz’

Word-of-mouth (WOM) is the process of conveying information from person to person and plays a major role in customer buying decisions (Richins & Root-Shaffer, 1988). It functions through social networking and trust, because people tend to share opinions, reactions and attitudes with members of their family, friends, and others in their social network whom they

16 perceive as trustworthy (Chevalier & Mayzlin, 2006). Concerning the quality of experience goods, information from others is valued because the product quality is unknown prior to consumption (Carare, 2012; Peltoniemi, 2014).

During the past decade, electronic word-of-mouth has substituted and complemented other forms of business-to-consumer and offline word-of- mouth communication about the quality of products (Chevalier & Mayzlin, 2006). It has been shown that people are influenced by online reviews of other customers, even when they are not acquainted. For instance, in their study on the effects of online user reviews, Duan, Gu, and Whinston (2008) found evidence that movies’ box office sales were significantly influenced by the volume of online reviews. They considered this awareness effect to online user reviews as an indicator of underlying word-of-mouth.

Besides online customer reviews, also ranking lists and sales charts are known to affect customer behaviour and buying decisions because they provide visibility (Yoo & Kim,

2012). It is widely accepted that charts attribute value to entities that would otherwise have remained unrevealed (Attali, 1985). For example, Sorensen (2007) found that book sales increase followed the rankings in the New York Times bestseller list. It was reasoned that appearing in a chart such as a bestseller list serves as a signal of quality. As the quality of experience and cultural goods are unknown to the consumer prior to consumption, ignorant potential consumers tend to believe that the rankings reflect other buyers’ perspective on the quality of the cultural good (that is, audience appeal). This phenomenon of people using information of popularity of products as a signal of quality is referred to as observational learning (Hedström & Swedberg, 1998).

Also Carare (2012) found that the public information about the past popularity of products in the form of bestseller lists significantly affects customers’ purchase decisions. His study on the causal impact of bestseller rank information of apps showed that the willingness

17 to pay of consumers was approximately $4.50 greater for a top ranked app than for the same unranked app. In addition, Salganik and Watts (2008) discovered that participants’ preferences were shaped by the music download choices of other participants in their experiment with an artificial online music market.

According to Scott (2012), online ranking charts are central to building buzz because they function as a template for comparing, valuing and ordering music producers, and thus they act as a proxy for popularity and market potential. He referred to buzz as “the infectious power of rumours and recommendations circulating through dense cultural intermediary networks” (p. 244), which implies that people are influenced in their tastes and purchasing decisions by their social environment. A positive buzz generates excitement and enthusiasm and can be used to form an audience, stimulate consumption, and generate marketable values

(Scott, 2012), and this is what word-of-mouth marketing seeks to achieve.

Becoming aware of and consuming music can be seen as a social process when people listen to music together and form opinions about music based on others’ assessments. This intangible social value formed from and within specific contexts helps to create a buzz around that particular art/culture (Caves, 2000; Currid, 2007), which influences the economic value of a cultural good such as music. Therefore, artists, labels, booking agencies and other cultural agents construct music communities online in order to provide a platform for buzz, since this is expected to lead to higher audience awareness and appeal.

2.5 Hypotheses

All in all, several determinants of consensus have been addressed in scientific literature. From a sociological point-of-view, it was indicated that justification and legitimation precede consensus (Baumann, 2007). From an ecological perspective, it was found that spanning multiple categories implies less effective communication of an organization’s fit with the

18 targeted positions (Hsu, 2006), which is expected to result in less consensus about the organization’s position in the market. Moreover, the lack of focus that comes with a wider niche width might prevent a clear identity from forming in the audience (McKendrick et al.,

2003; Negro et al., 2010). All things considered, it is assumed that successful justification and legitimation becomes more difficult to achieve as spanning increases. So, in context of the music industry, it is expected that the more genres are spanned, the more difficult it becomes to justify and legitimize the genre classification of artists, which should result in a lower consensus across audiences.

In addition, Phillips and Zuckerman (2001) argued that high-status actors have less of a need to conform to broad cultural codes in order to construct an identity that appeals to the audience, because their status affords allow them to deviate to some degree. Also Mattsson et al. (2010) argued that artists making their first entry are likely to face higher penalties by audiences if they deviate from existing genres. This could imply that high-status artists who are experienced in the music industry are less vulnerable to the negative effects of spanning on the genre consensus among audiences than newcomers. In the same line, Zuckerman et al.

(2003) found that the trade-off between a generalist or specialist identity is greater among novice actors, because novices have yet to go through the audiences’ selection process that differentiates the skilled from the unskilled. Altogether, it is hypothesized that:

H1a: Genre spanning has a negative effect on genre consensus.

H1b: This effect is moderated by the artist’s years of experience.

Nowadays, social media may significantly impact a firm’s reputation, sales, and even survival

(Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). In the context of artists, social media statistics such as the number of fans, followers, listeners and subscribers may serve as

19 relevant indicators of reputation and popularity. Social media facilitate knowledge sharing

(Yates & Paquette, 2011) and ranking lists provide visibility (Carare, 2012; Yoo & Kim,

2012). Thus, it is assumed that if artists appear on ranking lists and their social media profiles display high audience appeal, this would imply a considerable amount of word-of-mouth communication and buzz surrounding these artists and their genre profiles (Scott, 2012). An increase in dialogue and interaction (Cattani et al., 2008) by the right consumers, marketing strategists, critics and media could facilitate legitimation and justification of the artists’ genre profiles (Baumann, 2007) and spread status beliefs (Ridgeway & Correll, 2006), which should lead to a higher genre consensus across audiences. Thus, it is expected that;

H2: Audience appeal has a positive effect on genre consensus.

20

3. Method

3.1 Sample

The Electronic Dance Music (EDM) industry is chosen as empirical setting because it represents a largely neglected creative industry in scientific literature, despite its impressive growth during the past decade (EVAR, 2012). Disc jockeys (DJs) represent the most prominent actors in this industry, and the genre labels that are adopted to classify them have a tremendous influence on the EDM community (McLeod, 2001). It is even claimed that “the continuous and rapid introduction of new subgenre names into electronic dance music communities is equalled by no other type of music” (McLeod, 2001, p. 60). All this indicates the relevance of genre labelling for DJs in the EDM industry.

The sampling frame is derived from the DJ Mag, which is a monthly magazine dedicated to EDM that has been published since 1991. Its DJ Top 100 list represents the outcome of the world’s leading DJ poll which attracts over 350,000 votes a year. The poll asks the audience members to list their five favourite DJs. Thus, since the list is widely recognized because of its popularity and determined by public vote (EVAR, 2012), it can be assumed that it accurately displays which DJs are appealing in the eyes of the audience.

This study focuses on the DJs who have been listed in the DJ Mag Top 100 between

2010 and 2014 (appendix A on p. 59-62). Each DJ who appeared on the list at least once was included, which led to a sample of 187 DJs (appendix B on p. 63-64). The large sample size is considered to have a positive effect on the generalizability of the study, whereas the clearly specified sampling frame positively affects the reliability because it can be easily duplicated.

21

3.2 Data Collection

Secondary data were obtained from online DJ community databases, several social media websites, and commercial music platforms which display the popularity of DJs and genres.

This form of data collection was convenient because the raw data were easily accessible online and usually complete. Personal accounts were created if these were necessary to get access. Next, a dataset was built by integrating information taken from the artist pages of all

DJs (N = 187) from 11 online sources which are described in detail below.

3.2.1 DJ Databases

Genre spanning and genre consensus data were derived from four online DJ databases: DJ

Rankings, Partyflock, The DJ List, and Top Deejays. In order to determine the DJs’ years of experience, data were used from Discogs.

DJ Rankings

DJ Rankings is an online DJ community, established in Japan in 2012. The website provides a top 10,000 DJ list, which is based on an advanced algorithm that considers DJ fees and salaries, media presence, chart data from music releases, airplay from radio stations and followers on large social networks. Their expert jury team makes the final adjustments based on their perspective on the DJs’ technical skills and craftsmanship (DJ-Rankings, 2015). The list displays the artists’ name, nationality, and associated genres. The DJ rankings can also be selected per country or genre. In addition, the website hosts remix competitions and provides a DJ swap feature which connects resident DJs around the world.

Partyflock

Partyflock is an online Dutch dance community which was launched in 2001. Visitors can

22 create a personal profile, add dance events to their agenda, create a list of friends, add pictures and indicate their favourite genres. The website allows to search for a list of upcoming and past events per artist, venue, organization, or city, and contains numerous forums that function as a platform for its users to engage in dialogue with each other about music and events. It also offers interviews with artists, reviews about new music and the possibility to watch photos and videos taken at events. Partyflock has approximately 200,000 active members and around 750,000 unique visitors every month (Partyflock, 2014).

The DJ List

The DJ list is an online DJ database and platform based in the United States, dedicated to promotion and awareness of Electronic Dance Music. It contains over 510,000 DJ profiles and almost 900,000 registered users. Users can create a personal profile, follow their favourite

DJs, and are informed of EDM news, reviews, interviews, events and contests. The website also offers the possibility to filter DJs by country or genre, and displays each DJ’s global ranking and global ranking per genre. Furthermore, it recommends 10 related artists per DJ

(TheDJList, 2015).

Top Deejays

Top Deejays is an online DJ database, founded in Slovenia. The website uses an algorithm to calculate DJs’ social media influence by combining Facebook, Twitter, SoundCloud,

MySpace, Last.fm and YouTube fans, subscribers and followers, in order to construct a DJ ranking list. DJs can be filtered by country, genre or social network. The website also provides information about new artists and lists of the seven most popular genres and countries. Visitors can access and create DJ profiles which contain social media statistics, display five related artists and the DJ’s genre classification (TopDeejays, 2014).

23

Discogs

Discogs is the largest online user-built database and marketplace for mainly electronic music.

It contains almost 6 million releases, of almost 3.9 million artists, from almost 750,000 music labels. Discographies can be filtered per genre, style, format (e.g. vinyl, album, or CD), country, or decade. Artists’ profiles contain a biography, an overview of their discography

(e.g. albums, singles, compilations and DJ mixes), and links to the marketplace where related

CDs and vinyl can be purchased and sold. The website also offers user-created and managed groups which discuss music and other related subjects (Discogs, 2015).

3.2.2 Social Media & Ranking Lists

Statistics from social media sources such as Facebook, SoundCloud, Spotify, Partyflock,

Twitter and YouTube, as well as rank data from the DJ Mag were obtained in order to measure audience appeal.

Facebook

Facebook is the world’s most popular online social networking service with over a billion of registered users. The site allows its users to create a personal profile, post information about themselves, and leave messages on their friends’ profiles (Raacke & Bonds-Raacke, 2008).

Moreover, it is possible to upload pictures and videos, exchange private messages and keep in touch with friends, family and colleagues. Users can also create groups and events, or build a public page around a topic of interest.

SoundCloud

SoundCloud is a social music platform which allows users to create, share and discover sounds. The service is assessed by over 175 million users on a monthly basis. Visitors can

24 upload, listen, like and repost songs, follow their favourite artists, create a personal profile and access their listener statistics. The uploaded audio can easily be shared privately with friends or publicly with the entire SoundCloud community. To a certain extent, the service outwits music piracy as the songs can only be downloaded if permission is given by the artist

(SoundCloud, 2014).

Spotify

Spotify is a commercial music streaming service which provides the possibility to listen millions of tracks; legally and unlimited. Users can browse music by artist, album, genre and record label. It also includes other features such as creating and sharing playlists, or choosing a playlist which matches the user’s mood. Consumers can choose to either use the service for free, or pay a monthly “Premium” subscription which removes all advertisements, and provides the options to download music and listen offline. Spotify has over 15 million paying subscribers, over 60 million active users and offers more than 30 million songs (Spotify,

2015).

Twitter

Twitter is an online social network on which users can register and upload text-based posts of up to 140 characters (the so-called “tweets”). Each personal profile shows an overview of the person’s tweets, with the most recent one on top. Personal accounts are characterized by the

“@” in front of the name. Tweets can include words preceded by the symbol “#”, this hashtag is used to mark keywords. Clicking on a keyword directs the user to a complete list of all tweets containing that topic. Users can follow others or be followed themselves, and choose to either keep their tweets private or make them public. Moreover, Tweets can be retweeted, meaning that the message is copied onto the user’s personal profile and made available for the user’s followers, which is a very effective resource for electronic word-of-mouth (Jansen,

25

Zhang, Sobel, & Chowdury, 2009). Twitter connects 302 million active users every month and 500 million Tweets are sent on a daily basis (Twitter, 2015).

YouTube

YouTube is the world’s largest video-sharing website with billions of users. It offers visitors without an account the chance to watch videos and access video channels. A registered account allows to create a personal channel, subscribe to favourite channels, and upload, rate and discuss videos. Videos can be filtered based on categories such as popular, music, sport, games, films and news. The website also offers personalized recommendations based on the user’s viewing history and is actively used as a distribution platform of original content creators and advertisers (YouTube, 2015).

DJ Mag

DJ Magazine, or DJ Mag in short, is a monthly dance music magazine from the United

Kingdom. The related website offers Electronic Dance Music news, interviews and reviews, and publishes two Top 100 lists each year; one for DJs and one for clubs, both based on public votes (DJMag, 2010). Despite the criticism (after all, the list measures to what extent the DJs appeal to the audience, but not necessarily who the ‘best’ DJ is), the DJ Top 100 list is still the world’s most popular ranking list and widely recognized (EVAR, 2012).

26

3.3 Genre Classification System Development

The specificity of genre classifications varied across the four sources: (1) DJ Rankings, (2)

Partyflock, (3) The DJ List, and (4) Top Deejays (appendix C on p. 65). As each of the online databases and platforms was founded in a different country, the variation of genre classification systems was to be expected because artistic classification systems tend to differ across societies (DiMaggio, 1987). In order to accurately measure genre spanning and genre consensus, a genre classification system was developed.

First, it had to be determined how and by whom the classification systems on each source was constructed, whether the data were up-to-date, and who decided on the DJs’ genre profiles. Therefore, all platforms were contacted via e-mail or via a question form on the website itself. A representative of Top Deejays replied and indicated that the genres on their website are updated over time, and if applicable set according to the DJ’s personal request. In addition, the DJ profiles are created by audience members but compared to profiles on other social media websites by members of the Top Deejays team in order to confirm the profiles’ validity.

Despite repeated requests, no responses were received from the other platforms. Thus, in order to warrant the validity of the final genre classification system, two EDM experts were consulted concerning its development via telephone. Both experts had more than 10 years of working experience in the EDM scene, with organizing and promoting events, and also composing line-ups.

The process of data-cleaning occurred in several steps. First, the genres that clearly indicated the same but were spelled differently (for instance, ‘psy-trance’ and ‘psychedelic trance’) were aligned. This narrowed down the number of distinct genres to 46. An overview of the names that were changed is shown in table 1 (p. 28).

27

Table 1. Changes in Genre Classification Systems Based on Name Similarity

Source Original classification New classification

DJ Rankings Hardcore techno Hardcore / Hard techno

Indie / Underground Indie dance

Traditional house House

Partyflock Deephouse Deep house

Electro Electro house

Hardcore Hardcore / Hard techno

Progressive Progressive house

Techhouse Tech house

The DJ List Indie dance / Nu disco Indie dance

Psy-trance Psychedelic trance

Top Deejays Breaks Breakbeat

Hard techno Hardcore / Hard techno

Psy-trance Psychedelic trance

Next, it was assessed which genres were represented on two or more online sources. The four sources are all founded in different countries (Japan, the Netherlands, United States and

Slovenia). Thus it was reasoned that if a genre appears on at least two sources, this would indicate an international consensus on the classification to some extent. As displayed in table

2 (p. 29), only 16 genres appeared on multiple platforms and databases: (1) breakbeat, (2) deep house, (3) drum & bass, (4) dubstep, (5) electro house, (6) electronica, (7) hard dance,

(8) hardcore / hard techno, (9) house, (10) indie dance, (11 ) minimal, (12) progressive house,

(13) psychedelic trance, (14) tech house, (15) techno, and (16) trance.

28

Table 2. Genre Representation Across the Four Sources

Genres Number of sources Genres Number of sources

2-step 1 Hardcore / Hard techno 4

Acid 1 Hardhouse 1

Ambient 1 Hardstyle 1

Breakbeat 2 Hardtrance 1

Chill out 1 Hiphop 1

Classics 1 House 4

Club 1 Indie dance 3

Commercial dance 1 Jungle 1

Darkcore 1 Latin 1

Deep house 2 Minimal 4

Dirty house 1 Moombahton 1

Disco 1 Pop 1

Drum & bass 4 Progressive house 4

Dubstep 4 Psychedelic trance 3

Eclectic 1 R&B 1

EDM 1 Raw hardstyle 1

Electro house 4 Soul 1

Electronica 3 Tech house 4

Funk 1 Techno 4

Garage 1 Trance 4

Goa 1 Trap 1

Groove 1 Tribal house 1

Hard dance 3 Urban 1

29

In collaboration with the two EDM experts, it was assessed whether the remaining 30 genres could be classified as one of the 16 main genres. A list was made of the subgenres that could be labelled according to their main genre, which was approved of by the EDM experts. The eight changes that were made are displayed in table 3 below.

Finally, the genres that were left consisted of: Acid, Ambient, Chill out, Classics,

Club, Commercial dance, Disco, EDM, Eclectic, Funk, Garage, Groove, Hardhouse,

Hardtrance, Hiphop, Jungle, Latin, Pop, R&B, Soul, Trap, and Urban. These 22 genres could not be classified as one of the 16 main genres with certainty because of three reasons. First, a few represent an entirely different genre (e.g. Acid and Jungle). Second, a few genres had a dyadic character. For instance ‘Hardhouse’ shares characteristics with House but also with

Hard dance. Third, a group of genres could not be classified as a genre of Electronic Dance

Music in the first place (e.g. Funk, Hiphop, Latin and Urban). For these 22 genres, dummy variables were created (see p. 33).

Table 3. Changes in Genre Classification Systems Based on Subgenre Identification

Source Original Classification New Classification

Partyflock 2-step Breakbeat

Darkcore Hardcore / Hard techno

Dirty house House

Goa Psychedelic trance

Hardstyle Hard dance

Moombahton Electro house

Raw hardstyle Hard dance

Tribal house House

30

3.4 Variables and Measures

Independent variables

Genre spanning. Genre spanning is calculated by adding up the total number of distinct genres associated with every DJ across the four online databases; (1) DJ Rankings, (2)

Partyflock, (3) The DJ List, and (4) Top Deejays. For instance, if a DJ is classified as house, electro house, house, and progressive house, the total genre spanning is assigned a value of 3.

In addition, a total of eight variables were adopted to measure audience appeal.

Average ranking (reversed). This variable is calculated by taking the sum of the DJ Mag positions between 2010 and 2014, divided by the number of years that the DJ had a position on the list. Next, based on ordinal ranks Rt = [1, 100], a DJ's inverse listing (101 - Rt) approximates his yearly popularity (Keuschnigg, 2015). Using this approach, a higher score indicates higher popularity, which makes it easier to interpret the results. As an example,

Knife Party appeared on the list in 2012, 2013 and 2014. Across these three years, they obtained position 33, 25, and 53. The sum of these values, divided by three gives an average position of 37. Lastly, 101 – 37 = 64, which gives the final average ranking value.

Times ranked. This variable measures the number of times a DJ obtained a position on the DJ

Mag Top 100 list across five years (2010 – 2014). It is considered to be an indicator of audience appeal over time as the list is based on public vote.

Facebook likes. The number of likes on the DJ’s Facebook page.

Partyflock fans. The number of fans on the DJ’s Partyflock artist profile.

SoundCloud followers. The number of followers of the DJ’s SoundCloud profile.

Spotify followers. The number of followers of the DJ’s Spotify page.

31

Twitter followers. The number of followers of the DJ’s Twitter account.

YouTube subscribers. The number of subscribers on the DJ’s YouTube channel.

Dependent variable

Genre consensus. Genre consensus measures audiences’ consensus on a DJ’s fit with targeted genres across the four online databases; (1) DJ Rankings, (2) Partyflock, (3) The DJ List, and

(4) Top Deejays. Following Hsu (2006), the average pairwise similarity was calculated between each source using Jaccard’s similarity coefficient. This formula has the following form;

a JS = a + b + c

JS = the Jaccard similarity coefficient that lies between JS = 0 (dissensus) and JS= 1

(consensus); a = the sum of positive genre matches between the two pairs; b = the sum of genres which was mentioned by the first source, but not by the second; c = the sum of genres which was mentioned by the second source, but not by the first;

For instance, if a DJ is classified as trance and hard dance on website 1, but on website 2 categorized as trance, house and electro house, then JS = 0.25.

1 JS = 1 + 1 + 2

As the data were obtained from four different sources, the Jaccard coefficients for each of the six pairwise comparisons were calculated. Next, these six values were added up and then divided by six in order to find the genre consensus value for that DJ. For example, DJ Avicii

32 is classified as progressive house by DJ Rankings, as house by Partyflock, as house and progressive house by The DJ List, and finally as progressive house and electro house by Top

Deejays. Six pairwise comparisons are made as displayed in table 4 (p. 34). In the case of missing data, for instance if a DJ was classified on three of the four sources, only three pairwise comparisons were calculated and the sum was divided by three. Thus, listwise deletion was not used on purpose, in order to make utmost use of the data.

Moderator

Years of experience. This measure is based on data from the DJ’s profiles on the online music database Discogs. It is calculated by taking the year of artist entry, meaning the year of the first publication of recorded music by an artist (Mattsson et al., 2010) and subtracting this from the current year, 2015. Pinheiro and Dowd (2009) used a similar measure for human capital; their measure of experience involved the number of years elapsed since each respondent first began playing musical instruments. In this study, artist entry for each DJ is based on the year of the first registered release, which can be either a single or an album.

Control Variables

Number of archival sources. Following Hsu (2006), I controlled for the number of archival sources in which the DJs were classified, because the number of genres under which a DJ is categorized is likely to increase with the number of different sources in which the DJ is listed.

Unclassified genres. Genre dummy variables were included for the 22 genres that could not be listed in the genre classification system (see p. 30) in order to control for category effects

(Hsu, Negro, & Perretti, 2012). For each genre, the DJs (N = 187) were assigned a 1 if the genre in question appeared in the DJ’s genre profile, and a 0 if it did not.

33

Table 4. DJ Avicii Example Jaccard Similarity Coefficient Calculation

Genre Sources Jaccard Similarity Calculation

1. DJ Rankings – Partyflock 0

J = S = 0 0 + 1 + 1

2. DJ Rankings – The DJ List 1

J = S = 0.5 1 + 0 + 1

3. DJ Rankings – Top Deejays 1

J = S = 0.5 1 + 0 + 1

4. Partyflock – The DJ List 1 JS = 1 + 0 + 1 = 0.5

5. Partyflock – Top Deejays 0

J = S = 0 0 + 1 + 2

6. The DJ List – Top Deejays 1

J = S = 0.33 1 + 1 + 1

Genre consensus

0 + 0.5 + 0.5 +

JS = 0.5 + 0 + 0.33 = 0.31

6

34

4. Results

4.1 Descriptive Statistics

The sample included 187 DJs from 30 different countries. Only 4 DJ acts consisted of females, which is not surprising because the EDM scene has always been dominated by men

(McLeod, 2001). Most DJs are from the Netherlands (n = 45), followed by the United

Kingdom (n = 27), the Unites States (n = 23), Germany (n = 13), and Sweden (n = 12), from which can be derived that 67.2% of the DJs represent one of the top 5 countries. A complete overview is displayed below in figure 1.

Figure 1. Histogram: number of DJs per country

35

Genre Popularity

Table 5 below displays the genre frequencies and percentages per source. It shows that progressive house is the most popular genre with 263 classifications, followed by trance, electro house, house, and techno. Together, these five most popular genres represent 75.51% of all genre classifications. In addition, breakbeat is clearly the least represented genre in the sample.

Table 5. Genre Frequencies and Percentages per Source

DJ Rankings Partyflock The DJ List Top Deejays

Freq. % Freq. % Freq. % Freq. %

1. Breakbeat 0 0 3 .08 0 0 1 .03

2. Deep house 0 0 3 .08 0 0 2 .06

3. Drum & bass 4 1.7 6 1.6 4 1.6 4 1.1

4. Dubstep 5 2.1 8 2.1 6 2.4 6 1.7

5. Electro house 40 16.8 60 15.6 43 16.9 67 19

6. Electronica 4 1.7 0 0 3 1.2 11 3.1 7. Hard dance 8 3.4 17 4.4 8 3.1 17 4.8

8. Hardcore / Hard techno 8 3.4 2 .05 8 3.1 16 4.5

9. House 17 7.1 82 21.3 40 15.7 39 11.1

10. Indie dance 6 2.5 0 0 1 .04 7 2

11. Minimal 1 .04 6 1.6 1 .04 1 .03

12. Progressive house 52 21.8 51 13.2 60 23.6 100 28.4

13. Psychedelic trance 5 2.1 2 .05 5 2 5 1.4

14. Tech house 9 3.8 7 1.8 9 3.5 8 2.3

15. Techno 11 4.6 28 7.3 11 4.3 9 2.6

16. Trance 57 23.9 54 14 55 21.7 54 15.3

Total 227 95.4 329 83.16 254 100 347 98.6

Others 11 4.6 56 16.84 0 0 5 1.4

Total 238 100 385 100 254 100 352 100

36

Genre Spanning

The genre spanning varied across the four databases. On DJ Rankings (n = 181), the majority of the DJs (n = 124) were associated with only one genre, followed by a spanning of two genres for the remaining 57 DJs (M = 1.31, SD = 0.47). The genre spanning on Partyflock (n

= 167) ranged between values 1 to 5 which occurred most in descending order, and displayed one outlier of 8 (M = 2.3, SD = 1.2). Similar to DJ Rankings, the spanning on The DJ List (n

= 176) and Top Deejays (n = 184) differed merely between one or two genres. On the DJ List, the divide between a spanning of one (n = 98) or two genres (n = 78) was almost equal (M =

1.44, SD = 0.5). In contrast to DJ Rankings and The DJ List, DJs were related more often to two genres (n = 168) instead of only one (n = 16) on Top Deejays (M = 1.91, SD = 0.28). In addition, most DJs were related to a total of two, three or four different genres across the four sources (Mdn = 3, range = 8), as displayed in figure 2 below.

Figure 2. Histogram: genre spanning

37

Years of Experience

The DJs (N = 187) differed in their number of years of experience in the music industry from

1 to 30 years (M = 11.06, SD = 6.2), as is shown in figure 3 below. For illustration, years of experience appeared to be non-normally distributed with skewness of 0.56 (SE = 0.18) and kurtosis of -0.44 (SE = 0.35). However, tests of normality have little relevance in this study because “in large samples, they can be significant even for small and unimportant effects”

(Field, 2013, p. 184).

Figure 3. Boxplot: years of experience

Audience Appeal

The DJ’s reversed ranking positions differed between 2 and 99 (Mdn = 38, range = 97). The number of DJs who shared the same final average reversed ranking value did not exceed the value of 4. In addition, the DJs varied in their presence on the DJ Mag lists between 2010 and

2014 (M = 2.66, SE = 1.49). 31% had appeared only once, while 22.5% had been listed two times, and 18.7% five times (see table 6, p. 39).

38

Table 6. Times Ranked in DJ Mag Top 100 List 2010 – 2014

Times Ranked Frequency Percent Cumulative Percent

1 58 31 31 2 42 22,5 53,5 3 27 14,4 67,9 4 25 13,4 81,3 5 35 18,7 100 187 100

In general, the DJs had a tremendous amount of likes on their Facebook profiles (Mdn =

680186, range = 55197304). A more detailed look at the data showed that 36.9% of the DJs scored between 100000 – 500000 likes, and seven DJs even had over ten million likes on their artist profile.

The number of Partyflock fans was considerably less (Mdn = 472, range = 17755), which is probably due to the smaller amount of active users. In addition, 30.5% of DJs had between 101 and 500 fans, and only 3 DJs scored above 10000.

Third, 19 DJs had more than a million followers on SoundCloud (Mdn = 97602.5, range = 5625012), and 32.6% scored between 100000 and 500000 followers.

Fourth, eight DJs were followed by more than one million individuals on Spotify, and

33.7% had between 10000 – 50000 followers (Mdn = 29916, range = 6615991).

With regard to Twitter, the most popular category of 100000 – 500000 followers was represented by 36.9% of the DJs. Moreover, there were three DJs with over five million followers (Mdn = 146121, range = 17150364).

Sixth, the number of YouTube subscribers ranged from 144 to almost ten million

(Mdn = 37489, range = 9858756). In total, 14 DJs had more than one million subscribers on their artist channels.

39

Genre Consensus

Average consensus scores ranged between 0 and 1 (M = .52, SD = .21). Hereby, a value of 0 would imply there is no consensus at all, while a value of 1 would indicate pure consensus.

There was partial consensus on the genre categorization for the majority of DJs. For some, there was no consensus at all, see figure 4 below.

Figure 4. Boxplot: genre consensus

Table 7. Pairwise Consensus Comparisons Between Sources Source Comparisons N M SD

1. DJ Rankings – Partyflock 161 .36 .32

2. DJ Rankings – The DJ List 174 .87 .26

3. DJ Rankings – Top Deejays 179 .51 .31

4. Partyflock – The DJ List 159 .42 .31

5. Partyflock – Top Deejays 165 .42 .28

6. The DJ List – Top Deejays 174 .56 .31

40

Table 7 (p. 40) provides an overview of the six pairwise comparisons that were calculated in order to measure the average genre consensus between each pair of sources. It appeared that

DJ Rankings and The DJ List agreed more on the DJ’s genre profiles than any other combination of sources (M = .87, SD = .26).

Correlations

To study the antecedents of genre consensus, the correlation matrix is presented (table 8, p.

42). From the correlations it appeared that in general, genre consensus is only significantly related with the number of times a DJ is ranked and genre spanning. This is in line with previous research on the positive effects of visibility on popularity (Duan et al., 2008; Scott,

2012) and the negative effects of genre spanning (Hsu, 2006; Negro et al., 2010). However, contrary to my expectations, genre consensus did not correlate with audience appeal on social media or years of experience in the music industry. The matrix also shows that this latter variable correlates with times ranked, from which could be derived that DJs who have more industry experience appear more often on the DJ Mag Top 100 list.

With regard to the social media platforms, the table displays that the Facebook likes,

SoundCloud followers, Spotify followers, Twitter followers and YouTube subscribers are all significantly related, and also with average reversed ranking and times ranked. This signals that the higher and the more often a DJ is ranked, the more popular the DJ is on social media

(and vice versa). However, the number of Partyflock fans did not correlate with audience appeal measures of other social media platforms. This is presumably due to country bias, because Partyflock is mostly used by Dutch people. Another interesting fact is the significant negative correlation between years of experience in the music industry and SoundCloud followers. Perhaps SoundCloud is more actively used by well-established artists than newcomers.

41

Table 8. Mean, Standard Deviation and Correlations of Study Variables

M SD 1 2 3 4 5 6 7 8 9 10

1. Average ranking 41.65 24.7 (reversed)

2. Times ranked 2.66 1.5 .627**

3. Facebook likes 1929341.3 4942898.8 .457** .312**

4. Partyflock fans 1495.5 2541.5 .179** .294** .057

5. SoundCloud 483307.6 1094067 .373** .212** .317** -.077 followers

6. Spotify followers 199048.5 681265.1 .399** .295** .927** .034 .380**

7. Twitter followers 545681.1 1657972.1 .396** .302** .802** .024 .416** .738**

8. YouTube 306994 1108186.6 .390* .254** .821** .039 .448** .9** .616** subscribers

9. Years of experience 11.06 6.2 .033 .222** .104 .082 -.149* .082 .115 -.021

10. Genre spanning 3.2 1.2 .043 -.028 .089 -.105 -.009 .067 .040 .052 .120

11. Genre consensus .52 .21 .061 .213** -.101 .049 .039 -.079 -.079 -.075 .115 -.527**

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

42

4.2 Regression Analyses

4.2.1 Genre Spanning and Genre Consensus

First, it was hypothesized that genre spanning had a negative effect on genre consensus. As expected, the linear regression analysis (see table 9 below) showed a significant negative effect between genre spanning and genre consensus, β = -0.53, t(183) = -8.42, p < 0.01. In addition, genre spanning explained 28% of the variance in genre consensus scores R2 = 0.28,

F(1, 184) = 70.87, p < 0.01. Therefore, I found support for H1a.

Table 9. Linear Regression Analysis Genre Spanning and Genre Consensus

Variable N β R2 F

Genre spanning 187 -.53 .28 70.87

Second, it was predicted that the effect between genre spanning and genre consensus would be moderated by the number of years of experience in the music industry. In order to calculate moderation in SPSS, the process tool was installed (Hayes, 2013). A moderation analysis was done which included genre spanning as predictor, genre consensus at outcome variable and years of experience in the music industry as moderator, F(3, 182) = 19.57, p <

0.001. As displayed in table 10 (p. 44), the interaction between genre spanning and years of experience was not significant, which suggests that H1b is not supported.

43

Table 10. Moderation Model of Predictors of Genre Consensus

(95% bias corrected and accelerated confidence intervals reported in parentheses)

Variables b SE B t p

Constant .59 (.49, .54) 0.01 41.56 < 0.01

Genre spanning -.09 (-.12, -.07) 0.14 -6.78 < 0.01

Years of experience .01 (.001, .01) 0.002 2.64 < 0.01

Genre spanning x years of -.002 (-.01, .002) 0.002 -1.03 0.33 experience Note. R2 = .31

4.2.2 Audience Appeal and Genre Consensus

The correlation matrix (table 8, p. 42) showed that of all measures of audience appeal, only times ranked was significantly correlated with genre consensus (r = .213, p < 0.01).

A linear regression was done to calculate whether times ranked had a positive effect on genre consensus (see table 11 below). A significant effect was found between the number of times a

DJ appeared on the DJ Mag Top 100 list and the consensus of the DJ’s genre profile, β = 0.21, t(183) = 2.96 , p < 0.01. A small portion of variance in genre consensus scores was explained, R2 = 0.05, F(1, 184) = 8.74, p < 0.01. Thus, H2 is partially supported.

Table 11. Linear Regression Analysis Times Ranked and Genre Consensus

Variable N β R2 F

Times ranked 187 .21 .05 8.74

44

4.3 Robustness Checks

Several additional checks were performed in order to test the robustness of the results. First, I controlled for the number of archival sources. The DJs who had profiles on all four sources (n

= 156) formed the baseline group. 23% of the DJs was mentioned by three sources, followed by 7 % who were listed on two sources. Only one DJ appeared on merely one source, and was therefore automatically excluded from the analysis by SPSS. Table 12 below displays that for both genre spanning (β = -.2) and times ranked (β = -.03), the change in genre consensus scores goes down as a DJ changes from being included on four sources to only two sources.

Table 12. Linear Model of the Predictors of Change in Genre Consensus Scores

Variables Genre spanning Times ranked

b SE B β p b SE B β p

Constant -.89 .04 <.01 .47 .03 <.01

4 archival sources vs. 3 -.03 .04 -.2 .33 -.02 .05 -.03 .33 archival sources

4 archival sources vs. 2 -.27 .06 -.34 < .01 -.26 .08 -.23 <.01 archival sources

Second, as described before (p. 30), dummy variables had been created for each of the 22 presumably irrelevant genres that appeared in only one of the four original genre categorization systems (appendix C, p. 65) and could not be clearly classified. Still, regression analyses were executed in order to confirm the absence of any categorical effect. The results showed that the inclusion of the dummies did not drastically change the significant effects of genre spanning (β = -.59, p < 0.01) and times ranked (β = 0.15, p < 0.01) on genre consensus, which confirms the robustness of the previous results.

45

5. Discussion

5.1 Main Findings

As expected, it was shown that spanning genres leads to a lower genre consensus. There may be several reasons for this finding. First, the negative effect may be due to atypicality; spanning genres may lead to a blurred identity which confuses the audience and decreases their agreement on the DJ’s genre profile (Hsu, 2006; Negro et al., 2010). Second, the fuzzy categorical boundaries may have thwarted the process of reaching consensus about DJs’ genre profiles (Negro et al., 2010; Negro & Leung, 2013). Third, spanning may have negatively affected genre consensus through a perception of decreased expertise and skill (Hsu et al.,

2009; Kovács et al., 2015). Despite the negative significant causal relation, the appropriate reason for this causality could not be confirmed.

In contrast to expectations, years of experience in the music industry did not moderate the relationship between genre spanning and genre consensus. Perhaps, relatively new artists may still be able to achieve a high status within a short period of time, which would allow them as well to deviate from the cultural codes that audience members attributes to certain genre categories (Phillips & Zuckerman, 2001). If that is the case, maybe a status measure could moderate the relationship between genre spanning and genre consensus.

Moreover, appearances on a ranking list over time that measures public appeal, such as the DJ Mag Top 100 list, has showed to have a positive effect on genre consensus. It is reasoned that this effect stems from the idea that ranking lists provide visibility (Carare, 2012;

Yoo & Kim, 2012),which leads to an increase of word-of-mouth communication (Scott,

2012). The more vibrant this ‘buzz’ around a particular DJ, the easier it is for agents such as media, artist managers and booking offices to legitimize the genre profile of the DJ in question, and thereby positively affects genre consensus.

However, the average ranking position and other audience appeal measures from social

46 media did not significantly affect genre consensus. Probably, measures of audience appeal over time are more applicable for genre consensus, rather than social media statistics that are only measured once. In hindsight, average ranking position may have been irrelevant. Like

Carare (2012) who distinguished between ranked and unranked apps, making a division between DJs that are ranked and unranked may have yielded significant results instead.

5.2 Implications

First, the data show that spanning less genres is more beneficial for achieving genre consensus. This insight may be relevant for artists, labels and booking agencies to consider in artist branding decisions.

Second, it was found that repeated appearances on the DJ Mag Top 100 list had a significant positive effect on genre consensus. This could indicate that artists and all managerial actors involved should continuously pursue a position on ranking lists, rather than neglect such lists’ benefits.

Third, genre consensus has been treated as a desired outcome throughout this study. As genre spanning negatively affects genre consensus, it was argued that it would be beneficial for artists to span rather fewer than more different genres because of the positive effect on genre consensus among audiences. However, the idea that disagreement about an artist’s genre-membership among audiences increases word-of-mouth and thereby increases awareness is within realm of possibility as well (Duan et al., 2008). For illustration, Nagle and

Riedl (2015) found that high levels of disagreement among previously posted online reviews led to more future product reviews. The managerial implication which arises here is the trade- off between refraining from genre spanning and striving for genre consensus because this positively affects appeal, or using genre spanning as a strategy to create a buzz surrounding the artist in order to increase disagreement and thereby awareness.

47

In the same line, Berger, Sorensen, and Rasmussen (2010) discovered that negative publicity can increase purchase likelihood and sales by increasing product awareness.

“Although negative publicity hurt products that already had broad awareness, it helped products that were relatively unknown” (p. 824). This could indicate that disagreement about genre profiles may be beneficial for new artists, but not for well-established artists, which should be considered in artist branding decisions as well.

5.3 Limitations

A few limitations should be considered in this study. First, the Electronic Dance Music industry and its genres is a very specific case, and therefore the results cannot be generalized to other creative industries with certainty.

Second, it could not be determined whether DJ Rankings, Partyflock, The DJ List and

Top Deejays attribute the same meanings to each genre. Therefore, it was assumed that each genre label signifies the exact same set of features across the four different sources.

Third, only for Top Deejays it could be clarified that the genre classification system is updated over time. For the other three sources, it was merely assumed that their genre profiles are up-to-date.

Fourth, a downside of the usage of social media statistics is that they can be manipulated. That is, a portion of the likes and followers on social media may have been bought instead of earned due to genuine audience appeal. This represents an inevitable source of bias as those numbers are unlikely to ever be provided.

Fifth, while DJ Mag ranking positions over time were accessible and categorized per year, the time span for genre spanning and genre consensus could not be accurately determined. Moreover, the social media data were only measured at one point in time.

Because social media statistics change rapidly over time, their effects on genre consensus

48 could change over time as well.

Sixth, this study focussed only on successful and ranked artists. Perhaps a difference could have been identified between ranked and unranked artists with regard to the effects of genre spanning and audience appeal on genre consensus.

The final limitation is due to the blurry boundaries that are typical for genres and especially EDM genres (McLeod, 2001), in combination with the usage of the Jaccard coefficient. As an example, it could not be accurately assessed to which extent two genres such as deep house and house are similar. Using face validity, it can be argued that they share similar characteristics to a certain extent, while on the contrary it could be assumed that deep house and hard dance are more dissimilar. Still, the Jaccard coefficient treats these three genres are equally different.

5.4 Suggestions for Future Research

Several issues concerning genres in the field of Electronic Dance Music were beyond the scope of this study. First, because the results could not be generalized with certainty beyond the EDM industry, genre spanning and audience appeal could be investigated as antecedents of genre consensus in other creative industries.

Second, interviews with DJs, label managers, booking agencies, and owners of online music platforms and DJ databases could be conducted in order to identify which characteristics they attribute to certain genres.

Third, additional antecedents of genre consensus could be investigated, as well as other possible moderators between genre spanning and consensus such as status (Ridgeway &

Correll, 2006).

Fourth, longitudinal studies can be done which follow DJs and audience members and measure their perception of the DJs’ genre profiles over time. Here, the distinction could be

49 made between successful and upcoming DJs, or ranked and unranked DJs as well.

Finally, future studies could adopt other measures than the Jaccard coefficient in order to calculate genre consensus. For instance, the fuzzy Hamming distance measure may yield more accurate results when measuring genre spanning and consensus, because it does not simply count exact matches but considers near misses as well (Bookstein, Klein, & Raita,

2001).

50

6. Conclusion

Genre categorization systems play a large role in audiences’ evaluation of artists, and whether audiences agree or not may affect the artists’ careers. While genre consensus has extensively been discussed in combination with genre spanning and audience appeal (Hannan, 2010; Hsu,

2006; Negro et al., 2010), no prior literature had measured genre spanning and audience appeal as antecedents of genre consensus. Therefore, this study sought to answer the question:

“To what extent function genre spanning and audience appeal as the antecedents of genre consensus?”

A dataset was built using secondary data from numerous online DJ databases, social media and commercial music platforms. According to expectations, it was found that genre spanning resulted in a lower genre consensus. However, years of experience in the music industry did not moderate this relationship. In addition, appearing on ranking lists over time was found as an antecedent of genre consensus, in the sense that the more often a DJ appeared on a ranking list that measured audience appeal, the higher the genre consensus was. By contrast, the average ranking position over time did not significantly affect genre consensus, and neither did any of the audience appeal measures obtained from social media.

Hereby, this study contributed to theories of categorization and organizational ecology. Furthermore, it offered some valuable insights for artists, labels, and booking agencies. Finally, as discussed, there are still numerous directions for further research in the field of Electronic Dance Music genres which would be interesting and worthwhile to explore.

51

References

Attali, J. (1985). Noise: The Political Economy of Music (Vol. 16). Manchester, UK:

Manchester University Press.

Baumann, S. (2007). A General Theory of Artistic Legitimation: How Art Worlds are like

Social Movements. Poetics, 35(1), 47-65. doi:

http://dx.doi.org/10.1016/j.poetic.2006.06.001

Becker, H. S. (1982). Art Worlds. Berkeley, CA: University of California Press.

Berger, J., Ridgeway, C. L., Fisek, M. H., & Norman, R. Z. (1998). The Legitimation and

Delegitimation of Power and Prestige Orders. American Sociological Review, 63(3),

379-405.

Berger, J., Sorensen, A. T., & Rasmussen, S. J. (2010). Positive Effects of Negative Publicity:

When Negative Reviews Increase Sales. Marketing Science, 29(5), 815-827.

Berkenkotter, C., & Huckin, T. N. (1995). Genre Knowledge in Disciplinary Communication:

Cognition, Culture, Power. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Bookstein, A., Klein, S. T., & Raita, T. (2001). Fuzzy Hamming Distance: A New

Dissimilarity Measure. Combinatorial Pattern Matching (pp. 86-97). Berlin

Heidelberg: Springer.

Carare, O. (2012). The Impact of Bestseller Rank on Demand: Evidence from the App

Market. International Economic Review, 53(3), 717-742. doi: 10.1111/j.1468-

2354.2012.00698.x

Carroll, G. R. (1985). Concentration and Specialization: Dynamics of Niche Width in

Populations of Organizations. American Journal of Sociology, 90(6), 1262-1283.

Cattani, G., Ferriani, S., Negro, G., & Perretti, F. (2008). The Structure of Consensus:

Network Ties, Legitimation, and Exit Rates of US Feature Film Producer

Organizations. Administrative Science Quarterly, 53(1), 145-182.

52

Caves, R. E. (2000). Creative Industries: Contracts Between Art and Commerce. Cambridge,

MA: Harvard University Press.

Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book

Reviews. Journal of Marketing Research, 43(3), 345-354.

Currid, E. (2007). The Economics of a Good Party: Social Mechanics and the Legitimization

of Art/Culture. Journal of Economics and Finance, 31(3), 386-394.

Dalgic, T., & Leeuw, M. (1994). Niche Marketing Revisited: Concept, Applications and

Some European Cases. European Journal of Marketing, 28(4), 39-55. doi:

10.1108/03090569410061178

DiMaggio, P. (1987). Classification in Art. American Sociological Review, 52(4), 440-455.

Discogs. (2015). About Discogs. Retrieved May 3, 2015, from

http://www.discogs.com/about

DJ-Rankings. (2015). About Rankings. Retrieved May 4, 2015, from http://dj-

rankings.com/about_rankings/

DJMag. (2010). About DJ Mag. Retrieved May 3, 2015, from http://www.djmag.com/about

Duan, W., Gu, B., & Whinston, A. B. (2008). Do Online Reviews Matter?—An Empirical

Investigation of Panel Data. Decision Support Systems, 45(4), 1007-1016.

EVAR. (2012). Dance-onomics: The Economic Significance of EDM for the Netherlands.

Retrieved January 20, 2015, from https://www.amsterdam-dance-

event.nl/static/files/dance-onomics_economic-significance-edm-17102012.pdf

Field, A. (2013). Discovering Statistics using IBM SPSS Statistics. London: Sage, 4th ed.

Hannan, M. T. (2010). Partiality of Memberships in Categories and Audiences. Annual

Review of Sociology, 36(1), 159-181. doi: 10.1146/annurev-soc-021610-092336

Hannan, M. T., Pólos, L., & Carroll, G. R. (2007). Logics of Organization Theory: Audiences,

Codes, and Ecologies. Princeton, NJ: Princeton University Press.

53

Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process

Analysis: A Regression-Based Approach. New York, NY: Guilford Press.

Hedström, P., & Swedberg, R. (1998). Social Mechanisms: An Analytical Approach to Social

Theory. Cambridge, UK: Cambridge University Press.

Hirsch, P. M. (1972). Processing Fads and Fashions: An Organization-Set Analysis of

Cultural Industry Systems. American Journal of Sociology, 77(4), 639-659.

Hsu, G. (2006). Jacks of All Trades and Masters of None: Audiences' Reactions to Spanning

Genres in Feature Film Production. Administrative Science Quarterly, 51(3), 420-450.

Hsu, G., & Hannan, M. T. (2005). Identities, Genres, and Organizational Forms. Organization

Science, 16(5), 474-490.

Hsu, G., Hannan, M. T., & Koçak, Ö. (2009). Multiple Category Memberships in Markets:

An Integrative Theory and Two Empirical Tests. American Sociological Review,

74(1), 150-169.

Hsu, G., Negro, G., & Perretti, F. (2012). Hybrids in Hollywood: A Study of the Production

and Performance of Genre-Spanning Films. Industrial and Corporate Change, 21(6),

1427-1450.

Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter Power: Tweets as

Electronic Word of Mouth. Journal of the American Society for Information Science

and Technology, 60(11), 2169-2188.

Keuschnigg, M. (2015). Product Success in Cultural Markets: The Mediating Role of

Familiarity, Peers, and Experts. Poetics. doi:

http://dx.doi.org/10.1016/j.poetic.2015.03.003

Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social Media?

Get Serious! Understanding the Functional Building Blocks of Social Media. Business

Horizons, 54(3), 241-251.

54

Koosel, S. M. (2013). Artists and Digital Culture: (The Strain of) Self-Promotion in Social

Media. Media Transformations, 10, 56-72.

Kovács, B., Hannan, M. T., & Sorenson, O. (2015). Conceptual Spaces and the Consequences

of Category Spanning. Sociological Science, 2, 252-286.

Lamont, M., & Molnár, V. (2002). The Study of Boundaries in the Social Sciences. Annual

Review of Sociology, 28, 167-195.

Lena, J. C., & Peterson, R. A. (2008). Classification as Culture: Types and Trajectories of

Music Genres. American Sociological Review, 73(5), 697-718.

Mattsson, J. T., Peltoniemi, M., & Parvinen, P. M. T. (2010). Genre-Deviating Artist Entry:

The Role of Authenticity and Fuzziness. Management Decision, 48(9), 1355-1364.

McKendrick, D. G., Jaffee, J., Carroll, G. R., & Khessina, O. M. (2003). In the Bud? Disk

Array Producers as a (Possibly) Emergent Organizational Form. Administrative

Science Quarterly, 48(1), 60-93.

McLeod, K. (2001). Genres, Subgenres, Sub-Subgenres and More: Musical and Social

Differentiation Within Electronic/Dance Music Communities. Journal of Popular

Music Studies, 13(1), 59-75.

Nagle, F., & Riedl, C. (2015). Online Word of Mouth and Product Quality Disagreement

(May 5, 2015). Harvard Business School Research Paper No. 13-091. Retrieved from

http://dx.doi.org/10.2139/ssrn.2259055

Negro, G., Hannan, M. T., & Rao, H. (2010). Categorical Contrast and Audience Appeal:

Niche Width and Critical Success in Winemaking. Industrial and Corporate Change,

19(5), 1397-1425. doi: 10.1093/icc/dtq003

Negro, G., & Leung, M. D. (2013). "Actual" and Perceptual Effects of Category Spanning.

Organization Science, 24(3), 684-696. doi: 10.1287/orsc.1120.0764

55

Padgett, J. F., & Ansell, C. K. (1993). Robust Action and the Rise of the Medici, 1400-1434.

American Journal of Sociology, 98(6), 1259-1319.

Partyflock. (2014). Algemene Informatie. Retrieved May 3, 2015, from

http://partyflock.nl/info/general

Peltoniemi, M. (2014). Cultural Industries: Product–Market Characteristics, Management

Challenges and Industry Dynamics. International Journal of Management Reviews,

17(1), 41-68.

Phillips, J. D., & Zuckerman, E. W. (2001). Middle‐Status Conformity: Theoretical

Restatement and Empirical Demonstration in Two Markets. American Journal of

Sociology, 107(2), 379-429. doi: 10.1086/324072

Pinheiro, D. L., & Dowd, T. J. (2009). All that Jazz: The Success of Jazz Musicians in Three

Metropolitan Areas. Poetics, 37(5), 490-506. doi: 10.1016/j.poetic.2009.09.007

Porac, J. F., Thomas, H., & Baden‐Fuller, C. (1989). Competitive Groups as Cognitive

Communities: The Case of Scottish Knitwear Manufacturers. Journal of Management

Studies, 26(4), 397-416.

Porac, J. F., Wade, J. B., & Pollock, T. G. (1999). Industry Categories and the Politics of the

Comparable Firm in CEO Compensation. Administrative Science Quarterly, 44(1),

112-144.

Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the Uses and

Gratifications Theory to Exploring Friend-Networking Sites. Cyberpsychology &

Behavior, 11(2), 169-174.

Richins, M. L., & Root-Shaffer, T. (1988). The Role of Evolvement and Opinion Leadership

in Consumer Word-of-Mouth: An Implicit Model Made Explicit. Advances in

Consumer Research, 15(1), 32-36.

56

Ridgeway, C. L., & Correll, S. J. (2006). Consensus and the Creation of Status Beliefs. Social

Forces, 85(1), 431-453. doi: 10.1353/sof.2006.0139

Rosa, J. A., Porac, J. F., Runser-Spanjol, J., & Saxon, M. S. (1999). Sociocognitive Dynamics

in a Product Market. The Journal of Marketing, 63, 64-77.

Salganik, M. J., & Watts, D. J. (2008). Leading the Herd Astray: An Experimental Study of

Self-Fulfilling Prophecies in an Artificial Cultural Market. Social Psychology

Quarterly, 71(4), 338-355.

Scott, M. (2012). Cultural Entrepreneurs, Cultural Entrepreneurship: Music Producers

Mobilising and Converting Bourdieu's Alternative Capitals. Poetics, 40(3), 237-255.

doi: http://dx.doi.org/10.1016/j.poetic.2012.03.002

Shocker, A. D., Bayus, B. L., & Kim, N. (2004). Product Complements and Substitutes in the

Real World: The Relevance of “Other Products”. Journal of Marketing, 68(1), 28-40.

Sorensen, A. T. (2007). Bestseller Lists and Product Variety. The Journal of Industrial

Economics, 55(4), 715-738. doi: 10.1111/j.1467-6451.2007.00327.x

SoundCloud. (2014). Press Information & Resources. Retrieved May 3, 2015, from

https://soundcloud.com/press

Spotify. (2015). What is Spotify? Retrieved May 2, 2015, from

https://press.spotify.com/us/information/

TheDJList. (2015). About The DJ List. Retrieved May 3, 2015, from

http://thedjlist.com/about/

TopDeejays. (2014). About TopDeejays. Retrieved May 3, 2015, from

https://topdeejays.com/about/

Twitter. (2015). About Twitter. Retrieved May 3, 2015, from

https://about.twitter.com/company

57

Vergne, J. P., & Wry, T. (2014). Categorizing Categorization Research: Review, Integration,

and Future Directions. Journal of Management Studies, 51(1), 56-94.

Weber, M. (1978). Economy and Society: An Outline of Interpretive Sociology (Vol. 1 & 2).

Berkeley, CA: University of California Press.

Yates, D., & Paquette, S. (2011). Emergency Knowledge Management and Social Media

Technologies: A Case Study of the 2010 Haitian Earthquake. International Journal of

Information Management, 31(1), 6-13.

Yoo, B., & Kim, K. (2012). Does Popularity Decide Rankings or Do Rankings Decide

Popularity? An Investigation of Ranking Mechanism Design. Electronic Commerce

Research and Applications, 11(2), 180-191. doi: 10.1016/j.elerap.2011.12.007

YouTube. (2015). About YouTube. Retrieved May 3, 2015, from

https://www.youtube.com/yt/about/en-GB/index.html

Zelditch, M. (2001). Processes of Legitimation: Recent Developments and New Directions.

Social Psychology Quarterly, 64(1), 4-17.

Zuckerman, E. W. (1999). The Categorical Imperative: Securities Analysts and the

Illegitimacy Discount. American Journal of Sociology, 104(5), 1398-1438.

Zuckerman, E. W., & Kim, T. Y. (2003). The Critical Trade‐Off: Identity Assignment and

Box‐Office Success in the Feature Film Industry. Industrial and Corporate Change,

12(1), 27-67.

Zuckerman, E. W., Kim, T. Y., Ukanwa, K., & von Rittmann, J. (2003). Robust Identities or

Nonentities? Typecasting in the Feature‐Film Labor Market. American Journal of

Sociology, 108(5), 1018-1073.

58

Appendix A. Sampling Frame DJ Mag Top 100 from 2010 – 2014

2010 2011 2012 2013 2014

1 Armin van Buuren David Guetta Armin van Buuren Hardwell Hardwell Dimitri Vegas & Like 2 David Guetta Armin van Buuren Tiësto Armin van Buuren Mike 3 Tiësto Tiësto Avicii Avicii Armin van Buuren 4 Deadmau5 Deadmau5 David Guetta Tiësto Martin Garrix 5 Above & Beyond Above & Beyond Deadmau5 David Guetta Tiësto 6 Paul van Dyk Avicii Hardwell Dimitri Vegas & Like Mike Avicii 7 Gareth Emery Afrojack Dash Berlin Nicky Romero David Guetta 8 Markus Schulz Dash Berlin Above & Beyond Steve Aoki Nicky Romero 9 Ferry Corsten Markus Schulz Afrojack Afrojack Skrillex 10 Axwell Swedish House Mafia Skrillex Dash Berlin Steve Aoki 11 ATB Paul van Dyk Headhunterz Skrillex Calvin Harris 12 Sander van Doorn Axwell Swedish House Mafia Deadmau5 Afrojack 13 Infected Mushroom Gareth Emery Markus Schulz Alesso Blasterjaxx 14 Steve Angello Fedde Le Grand Gareth Emery W&W Dash Berlin 15 Dash Berlin ATB Steve Aoki Calvin Harris Alesso 16 Sebastian Ingrosso Sander van Doorn Paul van Dyk Nervo Deadmau5 17 Laidback Luke Headhunterz Nicky Romero Above & Beyond Showtek 18 Judge Jules Ferry Corsten Sander van Doorn Sebastian Ingrosso W&W 19 Afrojack Skrillex Aly & Fila Axwell Deorro 20 Aly & Fila Laidback Luke Alesso Aly & Fila DVBBS 21 Fedde Le Grand Infected Mushroom ATB Markus Schulz Nervo 22 Carl Cox Aly & Fila Ferry Corsten Daft Punk Zedd 23 Swedish House Mafia Steve Angello Axwell Headhunterz R3hab 24 Cosmic Gate Hardwell Dada Life Zedd Andrew Rayel

59

25 Bobina Arty W&W Knife Party Above & Beyond 26 Benny Benassi Sebastian Ingrosso Fedde Le Grand Swedish House Mafia Steve Angello 27 Sasha Benny Benassi Noisecontrollers Showtek Dyro 28 Simon Patterson Daft Punk Arty Andrew Rayel Aly & Fila 29 John Digweed Martin Solveig Laidback Luke Fedde Le Grand Axwell 30 Eric Prydz Kaskade Kaskade Dyro Dannic 31 Richie Hawtin Carl Cox Calvin Harris Laidback Luke Ummet Ozcan 32 Andy Moor Chuckie Orjan Nilsen Paul Van Dyk Diplo 33 John O'Callaghan Bob Sinclar Knife Party ATB Krewella 34 Roger Shah Calvin Harris Sebastian Ingrosso Angerfist Oliver Heldens 35 Kaskade Noisecontrollers Chuckie Dada Life Fedde Le Grand 36 Headhunterz W&W Zatox Kaskade Vicetone 37 Chuckie Pete Tha Zouk Coone Frontliner Angerfist 38 Bob Sinclar Dada Life Dimitri Vegas & Like Mike Steve Angello Paul van Dyk 39 Avicii Angerfist Cosmic Gate Sander Van Doorn Sebastian Ingrosso 40 Kyau & Albert D-Block & S-Te-Fan Porter Robinson Martin Garrix Headhunterz 41 DJ Feel Coone Wildstylez Porter Robinson Borgore 42 Moonbeam Steve Aoki Angerfist Ferry Corsten Kura 43 Joachim Garraud Cosmic Gate Infected Mushroom Chuckie Daft Punk 44 Daft Punk Bobina Daft Punk Krewella Markus Schulz 45 Lange Richie Hawtin Carl Cox Coone Frontliner 46 Sean Tyas Eric Prydz Nervo Carl Cox Kaskade 47 Eddie Halliwell Zatox Pete Tha Zouk Bobina Brennan Heart 48 Erick Morillo Tydi Martin Solveig Omnia Coone 49 James Zabiela Orjan Nilsen Brennan Heart Orjan Nilsen Infected Mushroom 50 Umek Andy Moor Tenishia Zatox Laidback Luke 51 Paul Oakenfold Roger Shah Zedd Gareth Emery Sander Van Doorn 52 Matt Darey John O'Callaghan Eric Prydz BingoPlayers Dada Life 53 Mark Knight Astrix Bobina Infected Mushroom Knife Party 54 Richard Durand Erick Morillo Madeon Eric Prydz Dillon Francis

60

55 Martin Solveig John Digweed John O'Callaghan Tommy Trash Yves V 56 tyDi Wolfgang Gartner DJ Feel Wildstylez Firebeatz 57 Hernan Cattaneo DJ Feel Steve Angello Arty Porter Robinson 58 Sven Väth Showtek Omnia R3hab ATB 59 Astrix Richard Durand Umek Madeon Carl Cox 60 Super8 & Tab Umek Wolfgang Gartner Vicetone Eric Prydz 61 Andy C Skazi AN21 Brennan Heart Gabry Ponte 62 Myon & Shane 54 Paul Kalkbrenner Tommy Trash DJ Feel VINAI 63 Marcel Woods Sasha Francis Davila Gunz For Hire MAKJ 64 Roger Sanchez Eddie Halliwel D-Block & S-Te-Fan Diplo Radical Redemption 65 Wally Lopez Moonbeam Tritonal Tenishia DJ Snake 66 Mat Zo Myon & Shane 54 Bingo Players Noisecontrollers Da Tweekaz 67 Marco V Judge Jules Psyko Punkz Mike Candys Noisecontrollers 68 Leon Bolier Matt Darey Shogun Antoine Carnage 69 Ronski Speed Paul Oakenfold Paul Oakenfold Quentin Mosimann Cosmic Gate 70 Wolfgang Gartner Alesso Benny Benassi Project 46 Diego Miranda 71 W&W Mark Knight Tydi Blasterjaxx Zatox 72 Boys Noize Sean Tyas Mat Zo D-Block & S-Te-Fan Quentin Mosimann 73 D-Block & S-Te-Fan Mat Zo R3hab Dillon Francis Tenishia 74 Dubfire Lange Quentin Mosimann Dannic Gareth Emery 75 Dirty South Joachim Garraud Wasted Penguinz Adaro Umek 76 John B Simon Patterson Dirty South Richie Hawtin Tiddey 77 Daniel Kandi Francis Davila Andrew Rayel Martin Solveig Zomboy 78 Arty Psyko Punkz Richie Hawtin Felguk Bl3nd 79 BT Dimitri Vegas & Like Mike Frontliner Myon and Shane 54 Orjan Nilsen 80 Boy George Wildstylez Myon & Shane 54 Cosmic Gate TJR 81 Pete Tha Zouk Roger Sanchez Heatbeat Heatbeat 3LAU 82 Fatboy Slim Hernán Cattaneo Thomas Gold John O'Callaghan Don Diablo 83 Skazi Tritonal Nero Wasted Penguinz Madeon 84 Paul Kalkbrenner DJ Vibe Roger Shah Tiddey Wolfpack

61

85 Pete Tong AN21 Feed Me Skazi Mike Candys 86 Bloody Beetroots Bloody Beetroots Mike Candys Da Tweekaz Quintino 87 Arnej Felguk Andy Moor Tenashar Borgeous 88 Joris Voorn Nero Ran-D Bob Sinclar Audien 89 Dada Life Juanjo Martin Richard Durand Benny Benassi Boy George 90 Noisecontrollers Boy George Felguk Stafford Brothers Richie Hawtin 91 Showtek Tenishia Paul Kalkbrenner Bl3nd Ferry Corsten 92 Laurent Wolf Sidney Samson Moonbeam Paul Oakenfold Code Black 93 Claudia Cazacu Dirty South Sean Tyas Mat Zo Heatbeat 94 Calvin Harris James Zabiela Bob Sinclar Diego Miranda Merk & Kremont 95 Luciano Marcel Woods Netsky DJs From Mars Wildstylez 96 Marcus Schossow Porter Robinson Neelix Matt Darey Bingo Players 97 Sied van Riel Sven Väth Mark Knight Umek The Chainsmokers 98 The Thrillseekers Brennan Heart John Digweed Solarstone John O'Callaghan 99 Justice Leon Bolier Da Tweekaz Ummet Ozcan Arty 100 DJ Vibe Boys Noize Project 46 Ran-D Felguk

62

Appendix B. Sample DJ Mag Top 100 A-Z N = 187

1 3LAU 43 Daft Punk 2 Above & Beyond 44 Daniel Kandi 3 Adaro 45 Dannic 4 Afrojack 46 Dash Berlin 5 Alesso 47 David Guetta 6 Aly & Fila 48 D-Block & S-Te-Fan 7 AN21 49 Deadmau5 8 Andrew Rayel 50 Deorro 9 Andy C 51 Diego Miranda 10 Andy Moor 52 Dillon Francis Dimitri Vegas & Like 11 Angerfist 53 Mike 12 Antoine 54 Diplo 13 Armin van Buuren 55 Dirty South 14 Arnej 56 DJ Feel 15 Arty 57 DJ Snake 16 Astrix 58 DJ Vibe 17 ATB 59 DJs From Mars 18 Audien 60 Don Diablo 19 Avicii 61 Dubfire 20 Axwell 62 DVBBS 21 Benny Benassi 63 Dyro 22 Bingo Players 64 Eddie Halliwell 23 Bl3nd 65 Eric Prydz 24 Blasterjaxx 66 Erick Morillo 25 Bob Sinclar 67 Fatboy Slim 26 Bobina 68 Fedde Le Grand 27 Borgeous 69 Feed Me 28 Borgore 70 Felguk 29 Boy George 71 Ferry Corsten 30 Boys Noize 72 Firebeatz 31 Brennan Heart 73 Francis Davila 32 BT 74 Frontliner 33 Calvin Harris 75 Gabry Ponte 34 Carl Cox 76 Gareth Emery 35 Carnage 77 Gunz For Hire 36 Chuckie 78 Hardwell 37 Claudia Cazacu 79 Headhunterz 38 Code Black 80 Heatbeat 39 Coone 81 Hernán Cattáneo 40 Cosmic Gate 82 Infected Mushroom 41 Da Tweekaz 83 James Zabiela 42 Dada Life 84 Joachim Garraud

63

85 John B 131 Porter Robinson 177 Vinai 86 John Digweed 132 Project 46 178 W&W 87 John O'Callaghan 133 Psyko Punkz 179 Wally Lopez 88 Joris Voorn 134 Quentin Mosimann 180 Wasted Penguinz 89 Juanjo Martin 135 Quintino 181 Wildstylez 90 Judge Jules 136 R3hab 182 Wolfgang Gartner 91 Justice 137 Radical Redemption 183 Wolfpack 92 Kaskade 138 Ran-D 184 Yves V 93 Knife Party 139 Richard Durand 185 Zatox 94 Krewella 140 Richie Hawtin 186 Zedd 95 Kura 141 Roger Sanchez 187 Zomboy 96 Kyau & Albert 142 Roger Shah 97 Laidback Luke 143 Ronski Speed 98 Lange 144 Sander van Doorn 99 Laurent Wolf 145 Sasha 100 Leon Bolier 146 Sean Tyas 101 Luciano 147 Sebastian Ingrosso 102 Madeon 148 Shogun 103 MAKJ 149 Showtek 104 Marcel Woods 150 Sidney Samson 105 Marco V 151 Sied van Riel 106 Marcus Schössow 152 Simon Patterson 107 Mark Knight 153 Skazi 108 Markus Schulz 154 Skrillex 109 Martin Garrix 155 Solarstone 110 Martin Solveig 156 Stafford Brothers 111 Mat Zo 157 Steve Angello 112 Matt Darey 158 Steve Aoki 113 Merk & Kremont 159 Super8 & Tab 114 Mike Candys 160 Sven Väth 115 Moonbeam 161 Swedish House Mafia 116 Myon & Shane 54 162 Tenashar 117 Neelix 163 Tenishia 118 Nero 164 The Bloody Beetroots 119 Nervo 165 The Chainsmokers 120 Netsky 166 The Thrillseekers 121 Nicky Romero 167 Thomas Gold 122 Noisecontrollers 168 Tiddey 123 Oliver Heldens 169 Tiësto 124 Omnia 170 TJR 125 Ørjan Nilsen 171 Tommy Trash 126 Paul Kalkbrenner 172 Tritonal 127 Paul Oakenfold 173 tyDi 128 Paul van Dyk 174 Umek 129 Pete Tha Zouk 175 Ummet Ozcan 130 Pete Tong 176 Vicetone

64

Appendix C. Original Genre Classification Systems

DJ Rankings Partyflock The DJ List Top Deejays Commercial dance 2-step Drum & bass Breaks Drum & bass Acid Dubstep Chill out Dubstep Ambient Electro house Deep house Electro house Breakbeat Electronica Drum & bass Electronica Classics Hard dance Dubstep Hard dance Club Hardcore / Hard techno Electro house Hardcore techno Darkcore House Electronica Indie / Underground Deephouse Indie dance / Nu disco Hard dance Minimal Dirty house Minimal Hard techno Progressive house Disco Progressive house House Psychedelic trance Drum & bass Psy-trance Indie dance Tech house Dubstep Tech house Minimal Techno Eclectic Techno Progressive house Traditional house EDM Trance Psy-trance Trance Electro Tech house Funk Techno Garage Trance Goa Groove Hardcore Hardhouse Hardstyle Hardtrance Hiphop House Jungle Latin Minimal Moombahton Pop Progressive R&B Raw hardstyle Soul Techhouse Techno Trance Trap Tribal house Urban

65