University of Amsterdam Faculty of Business and Economics Msc Business Administration Entrepreneurship and Management in The
Total Page：16
File Type：pdf, Size：1020Kb
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.