1
The effect of status on the relation between category spanning and
consumer appeal: The case of DJs in Electronic Dance Music
Amsterdam, June 24, 2016
Faculty of Business and Economics MSc Business Administration
Entrepreneurship and Management in the Creative Industries Final draft Master’s Thesis
Student: Laurens Hop 10259953 First supervisor: Bram Kuijken
2 Statement of originality
This document is written by Laurens Hop who declares to take full responsibility for the contents of this document. I declare that this text and work presented in this document is original and the no sources other than 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 and completion of the work, not for the contents.
3 Table of contents
I. List of Tables and Figures 3
II. Abstract 5
1. Introduction 6
2. Literature review 10
2.1 Categorization 10
2.2 Category spanning and consumer appeal 13
2.3 Status 16
2.3.1 The distinction between status and reputation 17 2.3.2 The distinction between status and legitimacy 18
3. Conceptual framework 19
3.1 The effect of category spanning on consumer appeal under 19
the condition of DJ status
4. Methodology 23
4.1 Empirical setting 23
4.2 Sample 25
4.3 Data collection 26
4.3.1 DJ Databases 26
4.3.2 Ranking lists and social media 29
4.4 Genre classification system 31
4.5 Variables and measures 36
5. Results 39
5.1 Descriptives statistics 39
5.1.1 Sample characteristics 41
5.2 Correlations 44
5.3 Regression analyses 46
5.3.1 Category spanning and consumer appeal 46
5.3.2 Regression analysis with moderator included 47
5.4 Robustness checks 49
6. Discussion 50
4
6.2 Implications of the study 53
6.3 Limitations 54 6.4 Future research 55 7. Conclusion 57 Reference list 58 Appendix A 66 Appendix B 67
5 Abstract
In the music industry, categorization plays an important role in giving meaning to an offering of an artist. When audiences agree upon an artists’ membership within a category this may lead to an increase in an appeal of the audience which benefits the artist. However, the effects of category spanning is still an underexplored subject. Therefore, this study strives to gain deeper insights by investigating whether category spanning negatively affects consumer appeal. Thereby, the effect of status on this relationship is investigated.
Despite the significant growth of the Electronic Dance Music industry over the last decade, it has been largely neglected in scientific literature. Therefore this study has been conducted in this industry focussing on DJs since they are seen as its most prominent actors. This study investigated 103 of the most outstanding EDM DJs who have been listed in the DJ Mag Top 150 in 2015. Data has been derived from four major online DJ databases combined with music platforms and social media. By executing a regression analysis the effect of category spanning DJs on consumer appeal moderated by DJ status was investigated. None of the hypotheses were accepted, which suggests that other moderating variables have a stronger effect on the relationship between category spanning DJs and consumer appeal. This study contributes to existing literature as the results provide interesting starting points for future research. Thereby important insights are provided for artists, labels, and other participants in the EDM industry.
6 1. Introduction
In this globalized world, consumers are flooded with choices when it comes to purchasing a product or service. Therefore, it is essential that consumers have an understanding of what kind of services or products you’re offering so they can give a meaning to it which helps in evaluating the product. Not only classical markets face this challenge, also other markets in the creative industries like music festivals are dealing with significant increase in supply (Currin, 2014; Koranteng, 2004). In this industry DJs are seen as the most prominent actors and the genres classifying them are seen to have a serious influence on this scene (McLeod, 2001). In order to raise understanding among consumers, producers make use of the STP-process, which is seen as the basis of marketing strategy. This means that, organizations, entrepreneurs, and in this case DJs, need to segment and target their costumer group prior to positioning themselves (Kotler, 2003). One important aspect in the positioning phase is categorization, entailing that products grouped in particular categories serve largely the same purpose (Hannan, 2006). Furthermore, researchers advocate that categorization is a big driver of product performance (Durand & Paolella, 2013; Hannan, 2010). When converting this to DJs, clearly positioning themselves in one music category results most likely in a positive appraisal of the targeted consumer. On the other hand, when someone fails to categorize himself properly, he faces a significant risk in terms of poor performance (Durand & Paolella. 2013; Hannan, 2010). This shows the relevance of categorization as a considerable
determinant of success. Even though the increasing focus on categorization and its effect on product/producer performance, categorization is still an underexplored topic. This is mostly due to the lack of clear terminology and taxonomy across the research field regarding categorization and its components. This is also advocated by Vergne and Wry (2014) who argue that this is especially the case due to the fuzziness of category boundaries, which makes it often difficult to study the effects of categorization. These boundaries are important for
7 observers to identify what does and does not belong to the particular category. In the context of EDM DJs, one might easily identify a ‘house’ DJ because house is a mature category with clear established boundaries. However, DJs who produce ‘trap’ might be more difficult to categorize since the genre ‘trap’ has only recently emerged. Therefore, no clear boundaries are yet established which results in fuzzy boundaries for the ‘trap’ genre. Category boundaries are highly heterogeneous (Lamont & Molnar, 2002), some might be more clear and
straightforward than others. Also disagreement audience members might occur when categories have fuzzy boundaries, which therefore makes it even harder to identify a category’s members (Lamont & Molnar, 2002). Another aspect which confuses the process even further are partial category members. These are concepts that bear some but not all of the defining characteristics of the category. According to Hannan (2010), social sciences lack developed conceptual and methodological tools for dealing with this partiality of category membership. Which, once again, results in different perceptions of categories and discourages the product’s or producer’s performance. The issues presented above, highlight the challenges of dealing with the effects of categorization and putting it into practice, which urges the need for more attention on the subject of categorization.
Also Kóvacs and Hannan (2011), who focus more on a specific side of categorization, namely category spanning, acknowledge the value of categorization. They state that the similarity between spanned categories determines the misunderstanding of identity which results from combining them. This implies that a high degree of similarity between spanned categories would result in a higher degree of identity match which increases the appeal of the consumer, which is vital for success in the context of EDM DJs. This consumer appeal can be affected by different constructs like individual characteristics influencing perception (Hsu, 2006; Kovács & Hannan, 2011) or market conditions like organizational identity or status (Kóvacs & Hannan, 2011). Especially the link between category spanning and status received
8 little attention in existing research. This is pointed out by inconsistent terminology and
competing views of status throughout the literature resulting in disagreement as no clear definition of status is there to be found (Piazza & Castellucci, 2014). Also the implications and effects of status remain unexplored according to Ridgeway and Correll (2006). Therefore, Kóvacs and Hannan (2011) suggest to investigate the effects of status in relation to category spanning.
Current studies have endeavoured to provide a basis for categorization theory. Despite the value and potential that categorization holds, it still remains very complicated for
managers to take into account the effects of categorization. This is due to a lot of loose ends which are uncovered in the theory. Despite the supported effect of category spanning on consumer appeal (Hsu, 2006), it is not known what conditions empower or disapprove this relationship. The effect of status on the relationship between category spanning and consumer appeal was pointed out by Kóvacs and Hannan (2011). Hence, this study will fill this gap in the literature and will provide more information about the relationship between category spanning and consumer appeal under the condition of status. Due to the growing impact and value of the EDM industry and in particular its DJs, this literature gap will be investigated in this context. Therefore, the following research question is being addressed in this study:
How does status affect the relationship between category spanning and consumer appeal in the context of EDM DJs?
To provide a conforming answer, this study will mainly make use of internet gathered data. In addition, the category spanning behaviour for nearly 200 DJs is being used in
combination with other websites measuring consumer appeal and the online status of EDM DJs. With the results of this study, managers from in particular creative industries gain deeper insights in how to properly position a DJ (or product/service) they assist, under the condition of status, which is a highly valued resource. Extending research in this area could help
9 managers, DJs, labels and booking agencies to determine which strategy to enhance in order to gain market share and excel. Thereby comes that this research strives to contribute to categorization theory by exploring the effects of category spanning and status on consumer appeal.
Subsequently, this paper will be followed up by a literature review, expressing and investigating the topic more in-depth. Thereupon, the conceptual framework is examined presenting the hypotheses in the end which belong to this research. From there, the research design and methodology are illustrated, followed by the results obtained from the analysis of the internet gathered data. Eventually, the paper will come to an end with a discussion of the research and a briefly formulated conclusion.
10 2. Literature review
This part of the paper focusses on the existing literature regarding the research question in order to gain a deeper insight in the research topic. In the first place, the literature based on categorization will be illustrated. Secondly, an aspect of categorization known as category spanning will be addressed. Then, the concept of status will be described thoroughly which is followed by an illustration of consumer appeal. In the end, a brief conclusion will be
developed pointing out the significance of this research.
2.1 Categorization
In order to evaluate a product, consumers must be able to give meaning to a product. This is where categorization comes in play. Observers group products, ideas, concepts and objects that serve more or less the same purpose in particular categories.. A lot of different definitions about the concept of category are used throughout the research-field. However, the one
illustrated by Hannan et al. (2007), seems to be one of the brightest: “a category is a class about whose meaning an audience segment has reached a high level of intentional semantic consensus” (Hannan et al., 2007, p. 69). Category members can be organizations, industries or consumers, but also products. Researchers in the field of organizational sociology agree that the effects of categorization have a significant effect on product performance (Durand & Paolella, 2013; Hannan, 2010; Hannan et al., 2007; Hsu & Hannan, 2005; Lueng & Sharkey, 2013; Negro, Hannan & Rao, 2010; Ruef & Patterson, 2009). This is due to the fact that it simplifies complex situations as it drives beliefs and expectations in regard of an
organization’s characteristics and behaviours (Durand & Paolella, 2013). Even though the complexity of making comparisons between organizations and products which are increasing in line with their attributes and numbers, categories are acting as lenses as they enable the
11 consumer to restrict the number of considerations to a smaller number of entities. Simply put, categories provide some sort of anchor which enables audience members to make judgements about value and worth (Vergne & Wry, 2014). Thereby, it turns out that consumers give greater support to purer category members, which indicates that the fit between a category member and its category is crucial (Durand & Paolella, 2013). In the context of the music industry, the attributes of an artist like its style of music are being compared to a collective system of social codes and categories when the artist enters the market according to Mattson et al. (2010). To illustrate this, a DJ can position himself as playing dubstep music, in case he matches the attributes and social codes of that category, it will meet the expectations of the consumer and will be perceived as a legitimate member of the dubstep category. But in case consumers perceive a variation in attributes, it will lead to lower consumer appeal (Hsu, 2006) and could severely damage the DJ’s career. Therefore one should carefully make use of categorization practices in the positioning phase. As a consequence, consumers are able to compare offerings (Shrum, 1991) and it enables producers to recognize competitors (Clark & Montgomery, 1999; Porac et al., 1995).
In organizational categorization theory, Vergne and Wry (2014) made a distinction between two types of theory: categorical imperative and self-categorization. Firstly, this study will look at the former. Classic theory sees category attributes as criteria to determine whether something or someone belongs to the category or not (Vergne & Wry, 2014). Thus, taking this into practice, the Oxford English Dictionary defines a chair as ‘a separate seat for one person, with a beg and four legs’. This implies that any type of furniture which fits within this definition belongs to the category ‘chair’. At the same time, types of chairs that do not possess those particular characteristics, for instance three legged chairs, are not identified as producers even though we would recognize them as being a variant on the concept of ‘chair’. According to Quine (1951) one should be aware the categorization of an item may be imperfect or
12 incomplete. Despite this stating, he criticized this definition-based classical view for being too inaccurate and constraining. As a consequence of such criticism, an alternative view to the classic theory emerged, known as the prototype theory (Rosch & Mervis, 1975). This view focusses on the resemblances between objects instead on them having identical attributes. Rosch (1975) argues that every category should have a prototype, which are seen as pure types that possess all the coding clues of one single category. This would ease the process of category definition and enables the consumer to easily differentiate a category from one another. Thus, she advocates that consumers prefer highly prototypical objects since they fit the background expectations of the category they belong to. The prototype theory was fundamental to Zuckerman (1999) who evoked the categorical imperative. In his study he found that the offers of organizations who differentiate too much, lack clarity and
attractiveness which resulted in a lower amount of positive reviews and being systematically overlooked by market analysts contrary to more focused organizations. As a consequence, this led to more volatile share prices (Zuckerman, 1999).
The categorical imperative treats categories as the components of its external environment and are linked to the expectations which are imposed by audiences such as consumers, employees, regulators and critics. The core idea of the categorical imperative is that producers (e.g. organizations, producers or artists) are expected to follow the set of properties and rules which audience members (e.g. consumers, employees, regulators or critics) have attached to a categories by means of labels. At first, categories transfer the cultural code which are associated with belonging to that particular category, then audience members should determine which category an organization fits into and whether this is in line with their expectations (Vergne & Wry, 2014).
The self-categorization perspective reasons from cognitive psychology. This approach describes how organizations that possess common attributes perceive themselves as being in
13 the same category (Vergne & Wry, 2014). In terms of attributes, it focusses on aspects such as strategic co-optation, power, interest seeking and politics (Porac, Wade & Pollock, 1999). This approach is more producer oriented. By imitating the behaviour of other organizations and the strategic use of communicative tools producers tend to pursue self-selection. Nevertheless, it might be hard to seek membership in an existing category as category boundaries remain unclear in many cases (Vergne & Wry, 2014).
Even though the different views on categorization, category boundaries change over time and even differ across different types of consumers (Durand & Paolella, 2013; Vergne & Wry, 2014). Not only researchers dispute in regard of categories, also fans, critics, artists and other audiences are in an ongoing debate (Lena & Peterson, 2008). According to Lamont and Molnár, these interactions are the main reason why categories emerge, change and vanish as part of a continuous process (Lamont & Molnár, 2002). The emergence of subordinate
categories goes hand-in-hand with this process. According to Lena and Peterson (2008), these subordinate categories are seen as highly volatile since they compete for the same fans, media attention and legitimacy making it an important process to behold for other subordinate categories. This brings us to the next paragraph illustrating the spanning of categories. b
2.2 Category spanning and consumer appeal
Category spanning is part of the categorization research field. Vergne and Wry (2014) define category spanning in their research as “simultaneous membership in two or more categories located at the same level of the classification hierarchy”. This implies that being a Techno and House DJ at the same time can be seen as spanned categories. However this straightforward example, researchers have different views regarding the outcome of category spanning on performance. In its broadest sense, when spanning multiple categories, one widens its niche width. A niche is seen as a small group of customers which have similar preferences and
14 needs (Dalgic & Leeuw, 1994). Since the customer group of a niche is mostly small, an organization strives to do all they can to meet their needs and wants sufficiently. Some state there are positive outcomes in play where others argue for the negative consequences.
When looking more closely at these outcomes, Hsu et al. (2009) found that products exerting in category spanning face lower consumer appeal explaining social and economic setbacks. This result comes from a poor fit between the multiple categories of the product and the category expectations of the consumer. Therefore, categories should make sense of
products. However, the influence on this fit can be partly derived from the similarity of
spanned categories. So positioning yourself as a future-house and progressive-house DJ is less likely to result in a misfit between the producer and consumer perception of the category than contrasting categories. Thus, the extent to which a consumer perceives an offering as
intrinsically appealing depends on how well the offering matches the consumer’s taste so that it meets the consumer’s expectation of the category (Hannan, 2010).
According to Carroll (1985), the width of a niche measures the range of environmental dimensions across which organizations operate. In the field of categorization theory,
researchers make the distinction between specialists and generalists (Dobrev, Kim & Hannan, 2001; Dowell & Swaminathan, 2000; Freeman & Hannan, 1983; Hannan & Freeman, 1989; Hsu, 2006). The former focusses only on one category as the latter focusses on a wider scale of categories and engages in category spanning.
Research has found that specialists are more likely to outcompete generalists in the niches they target. This is mainly due to the fact that specialists focus their capacities on performing one specific activity opposed to generalists who divide their capacities across a variety of activities. As a consequence, specialists are able to perform this specific task more effectively and reliably. However, generalists are likely to outlast specialists since they are able to mitigate risk by spreading this across multiple areas they participate in (Dobrev, Kim
15 & Hannan, 2001; Dowell & Swaminathan, 2000; Freeman & Hannan, 1983; Hannan &
Freeman, 1989). Also, this allows for a certain degree of flexibility (Zuckerman et al., 2003). Furthermore, Pinheiro and Dowd (2009) found that category spanning in the context of jazz music had a positive effect on the earnings and recognition of the musicians. This principle of allocation is therefore important since it affects performance. Before choosing how to allocate your resources, it is important that the offerings of the organizations fit consumer’s
perception, the so called engagement. Hsu (2006) advocates that the level of engagement drives consumer appeal.
According the opponents of category spanning, focussing on a broader audience, or multiple segments entails that less attention is paid to establish a clear fit to each and is therefore more ambiguous in terms of communication to the consumers. In the first place, Negro and colleagues argue that when communication in regard of categories becomes ambiguous, category boundaries become more fuzzy and vague, which encourages disagreement about the category resulting in lower consumer appeal (Negro et al., 2010). Secondly, it is argued that ambiguous communication confuses the consumers (Hsu, 2006). As a consequence, the likelihood increases that the offerings are not in line with the
consumers’ expectations and perceptions of a particular category. This is seen as partiality of category membership making it harder for consumers to interpret the identity of the
organization or producer (Hsu, 2006; Negro, Hannan & Rao, 2010). These ambiguous identities lead to a mismatch between the offering and the consumers’ expectations, which negatively affects consumer appeal (Hsu, 2006). Furthermore, as mentioned before, one with blurred identities are mostly overlooked by the audience as pointed out by Zuckerman (1999). On the other hand, consumers will experience a higher level of agreement in fit between audience’s expectations with targeted positions when an organization has clearly established itself in terms of identity. Beyond, Hsu, Hannan and Kocak (2009) state that category
16 spanners are more likely to develop less expertise in each single one of the categories
compared to category specialists. This would lead to an increasing likelihood of misfit
between the consumer’s expectations and the offering with a decrease in consumer appeal as a result. Thereby comes that the ones who possess considerable expertise in each spanned category face the challenge of convincing the consumer of this (Kovács, Hannan & Sorenson, 2015). Though, Hsu and colleagues (2009) delivered empirical evidence that it was not due to the challenge of convincing the consumers but rather due to them not being perceived as legitimate members of the category. In terms of identity, status may act as an indicator communicating and shaping ones identity. The construct of status will be extensively described in the following paragraph.
2.3 Status
The status construct has been one of the main research topics in the field of social sciences for centuries. It almost dates from the birth of social sciences themselves (Barnard, 1938;
Maslow, 1943; Vroom, 1964) and has been identified as a type of inequality which has a twofold dimension. On the one hand, it can be seen as a relationship between social groups, like status differences between racial groups, occupations or gender (Ridgeway & Erickson, 2000; Weber 1987). On the other hand, Skvoretz and Fararo (1996) argue that status describes the hierarchical relationship among individuals taken into practice in terms of differences in influence or deference. This underlines the far stretching, sometimes ambiguous, area that the concept of status stretches. However, only a few decades ago, researchers found that status dynamics helps explain various misunderstood phenomena between and within organizations. These phenomena include for instance discrimination on the job, alliance formation and organizational change (Piazza & Castellucci, 2014). According to Sauder, Lynn and Podolny (2012) status is ‘meant to signal the particular category that an individual or an organization
17 occupies within a well-defined social hierarchy.’ Having a high status often comes with advantages since an individual with a high status might be perceived as a better performer (Lynn, Podolny & Tao, 2009). Additionally, Podolny argues that high-status organizations might be considered to be of a higher quality than low-status organizations (1993). Since there is no clear definition for the concept of status, the description in the review paper by Piazza and Castellucci (2014) is proposed. They encompass the following: ‘it is a subjective
judgment of social rank based on a hierarchy of values, and it is through status that the societal hierarchy of values is translated in practice to form the actual social order by means of status-organizing processes.’
Status is often confused with two other concepts, namely reputation and legitimacy. To gain a clearer understanding of status it might be worthwhile to point out the distinction between status and these other social evaluations of organizations (Bitektine, 2011).
2.3.1 The distinction between status and reputation
Among researchers in the economic and sociological field reputation is seen as ‘a signal that
allows external audiences to predict future behaviour performance, or quality of actors based on their previously observed behavior, performance, or quality’ (Camerer & Weigelt, 1988;
Raub & Weesie, 1990; Shapiro, 1983; Weigelt & Camerer, 1988; Wilson, 1985). Throughout the years, different views concerning reputation and its conceptualization are proposed to point out the distinction with status. Washington and Zajac (2005) clarified this distinction by stating that status describes the differences in agreed-on social ranks which generate
privileges not linked to performance whereas reputation entails differences in quality or merit generating rewards which are specifically based on performance. Once formed, status
ordering is slower rendered by change, although an ordering in reputation can be determined by an actor’s past performance or quality. This is verified by Ertug and Castellucci (2013),
18 who have shown in their research that status and reputation also have empirically different consequences on organizational outcomes next to conceptual differences.
2.3.2 The distinction between status and legitimacy
The concept of legitimacy finds its origin in the idea that the values of the organization have to be concurring with societal values if the organization wants to have a claim on resources provided by the society (Parsons, 1960). The extent to which an organization’s activities are aligned with socially acceptable or desirable activities proposed by either industrial norms or societal expectations in its broadest sense determines the level of legitimacy (Scott, 2001; Suchman, 1995). This implies that organizations whose structure fits public norms (Meyer & Rowan, 1977), who is being reviewed by analytical experts (Zuckerman, 1999), or whose human capital show acknowledgeable experience (Higgins & Gulati, 2003) are recognized to have accomplished a high level of legitimacy. Thus, legitimacy focuses more on the level of congruence on actor’s activities to what is expected of the actor by the society regarding these activities, whereas the focus of status is more on how these activities provide a starting point to determine a rank order of actors (Piazza & Castellucci, 2014).
19 3. Conceptual Framework
Previously, the existing literature regarding category spanning as a form of categorization has been explored, same as the status construct which will be addressed as a conditional effect on the relationship between category spanning and consumer appeal. At first, the expectations in regard of the direct effect between category spanning and consumer appeal will be stated. Secondly, the expectations regarding the conditional effect of status on the relationship between category spanning and consumer appeal will be illustrated. All expectations are followed by the accompanying hypotheses. Finally, to clarify and support the hypotheses, a conceptual model will be presented.
3.1 The effect of category spanning on consumer appeal under the condition of DJ status
As mentioned previously, this study will focus on the conditional effect of status on the relationship between category spanning and consumer appeal in the context of EDM DJs as suggested by Hsu (2006), Kovács and Hannan (2011). Recent research has shown that categorization is seen as an important aspect in the positioning phase of a product or service since it provides some sort of anchor which enables producers and consumers to assess the category’s value (Vergne & Wry, 2014) and as a result simplifies complex situations in terms of making comparisons (Durand & Paolella. 2013). Therefore, categorization is seen as an important driver of product or producer performance (Durand & Paolella, 2013; Hannan, 2010; Hannan et al., 2007; Hsu & Hannan, 2005; Lueng & Sharkey, 2013; Negro, Hannan & Rao, 2010; Ruef & Patterson, 2009). If someone aims to widens its niche width that he is operating in, this is known as category spanning. It is important that the attributes of the offerings of the product or producer match the perception and expectations of the audience, this level of engagement affects consumer appeal which is linked to product/producer
20 performance (Hsu, 2006). Since EDM DJs are highly dependent on consumer appeal in order to get booked, it is crucial that the fit between a category member and its category is high as the audience gives greater support to a ‘purer’ category member (Durand & Paolella, 2013). Not only this fit is important, also market conditions like organizational identity or status are seen to have an effect on consumer appeal (Kóvacs & Hannan, 2011). Therefore, following the suggestion of Kóvacs and Hannan (2011) this study will investigate whether the effect of status of an EDM DJ who is spanning categories will lead to positive consumer appeal.
As derived from the literature which has been reviewed before, producers who span in categories are more likely to suffer from lower consumer appeal due to the audience having difficulty interpreting their identity which increases the likelihood of ignoring the producer (Hsu, 2006). Hence, the expectation raises that DJs who span categories will have a negative influence on consumer appeal. Consequently, the following hypothesis is derived from this assumption.
Hypothesis 1: EDM DJs that span multiple categories will receive lower consumer appeal.
The status construct describes differences in agreed-on social ranks (Washington & Zajac, 2005) and is seen to focus more on how activities provide a starting point to determine a rank order of actors (Piazza & Castellucci, 2014). It is argued that organizations with a high status might be considered to have a higher level of quality than low-status organizations (Podolny, 1993). So, status is seen as some kind of signal that communicates a rank order or level of quality. Once formed, the status ordering is slowly rendered by change and is therefore quite persistent (Ertug & Castellucci, 2013). This status signalling might help the audience to interpret the identity of the producer more consistently and might consequently
21 shape the audience’s expectations of the offering more precisely. Considering the fact that interpreting the identity of the producer correctly by the audience is crucial for consumer appeal, the expectation raises that EDM DJs who span categories will receive consumer appeal when having a high status. In other words, when DJs are perceived to have a particular status, it is expected that this status decreasess the hypothesized negative effect of DJ category spanning on consumer appeal. Therefore, the following hypothesis is stated:
Hypothesis 2: Category spanning DJs will receive higher consumer appeal when possessing high status in the context of EDM
Finally, to clarify and support the hypotheses, a conceptual model is composed. This is shown in figure 1 on the next page.
22 Figure 1 – Conceptual Framework
Category spanning Consumer appeal
23 4. Methodology
In the previous sections, the existing literature in regard of the research question and
conceptual framework, including the hypotheses, has been illustrated. The design and method used to test the presented hypotheses will be discussed in the following section. Firstly, the empirical setting will be presented. Subsequently, the research sample will be discussed, followed by the measures used for the variables.
4.1 Empirical setting
In 2011 DJ-group Swedish House Mafia managed to sell out Madison Square Garden in only 9 minutes. At that time, it was the first DJ-act ever to headline one of the most iconic venues in the US. This served as the launch of the takeover of Electronic Dance Music (known as EDM). As a consequence, more EDM-songs started to hit the music charts, showing that EDM had the potential to become the world’s trending music genre. This growing awareness and popularity of the scene is affiliated with significant increases in the industry’s revenue. Not only more DJs entered the market, but also music festivals hosting these DJs popped up everywhere. To disclose the financial extent of the industry, the global Electronic Dance Music industry reached a value of $6.9 billion over 2014. Which represented a growth of 12% in comparison to 2013’s value of $6.2 billion (IMS Business Report 2015, 2015).
Additionally, this is accompanied by the growing footprint of DJs as revealed by Forbes in their ‘Electronic Cash Kings 2015’ list. It shows that the top DJs accounted for $272 million over 2014, which is more than double the top 10 received in 2012 which was set at $116 million. Remarkably, the revenues of the top 10 DJs with the highest revenue stream have seen their revenues increase by 108% in 2013, followed by a 11% growth from 2013 to 2014, and still a 2,5% increase from 2014 to 2015. Even though the revenue growth of the top 10
24 wealthiest DJs is slowing pace, they still account for large amounts of money. Calvin Harris finds himself on top of the list with $66 million almost duplicating the revenue of number two David Guetta who generated $37 million (“The world’s highest paid DJs,” 2015). This
illustrates the growing dominance and importance of the Electronic Dance Music (known as EDM) industry which instantly benefits the disc jockeys (DJs). These DJs and genres that are adopted to categorize them are seen to have a significant influence on the EDM community as they represent the most outstanding actors in the industry (McLeod, 2001). The potential of the industry have been acknowledged by investors. As a result, big conglomerates like SFX Entertainment set a foothold in this industry. Aiming to expand their dominance in the EDM industry by gathering ownership of various EDM promotors and festivals. The Electronic Dance Music industry has not yet reached their full potential according to statistics presented by Google. They reveal that subscribers to EDM channels on YouTube have risen
tremendously, a 60% growth on smartphones, 77% TV growth and 175% on game consoles. Additionally, 9 EDM labelled records in the Billboard top 100 in 2014 have grossed 2.3 billion views on official YouTube channels. As a consequence, records are hitting pop radio stations situated all over the world. This represents a notable shift from electronic dance music to the mainstream (“Google releases new statistics,” 2015). Despite the substantial growth over the last couple of years, the EDM industry have been largely neglected in the current literature. All this shows the relevance of DJ category spanning in EDM. Therefore, in order to contribute to the literature the EDM industry will be used as the empirical setting of this research (EVAR, 2012).
The objective of this research is to gain an accurate profile of persons, events or situations, therefore it is characterized as descriptive (Saunders et al., 2011, p. 171).
Furthermore, an archival research strategy will be used, which makes use of administrative records and documents as the principal data source. These documents can be historical as well
25 as recent (Saunders et al., 2011). One constraint of this strategy could be access to the data. However, the data will be retrieved from websites which are open to public, resulting in the needed data to be widely accessible. Since this research is constrained by time, a cross-sectional study is conducted, entailing a ‘snapshot’ time horizon.
4.2 Sample
In order to answer the research question, EDM DJs are used as the research population. The sample is derived from the DJ Mag, a monthly magazine, which was first printed in 1991 and is dedicated to dance music. The magazine is available through various channels including newsagents worldwide, online pdf and subscription. They add over 250 new reviews every month written by DJ Mag journalists. The review database of DJ Mag’s music section is one of the world’s most complete databases and is seen to be accurate and independent. They host an annual DJ poll starring the world’s leading DJs. The DJ Mag top 100 receives over
350,000 votes a year and has over 10,000,000 visitors viewing the results each year. In order to vote, the audience is asked to list its top five favourite DJs. According to EVAR (2012) the DJ Mag top 100 is widely recognized. More importantly, since it is determined by public vote, this list can be seen as a fairly accurate measure for displaying the appeal of the consumer regarding EDM DJs.
In order to gather enough data, the sample frame is composed out of DJs who have been listed in the DJ Mag top 150 in 2015. However, data regarding consumer appeal and status was not available for every case. Consequently, this resulted in a sample of 103 DJs (Appendix A). In order to be able to generalize the results, it is crucial to draw a reliable sample from the population. The likely error in generalizing the results to the population decreases as the sample size increases (Saunders et al., 2011, p. 265). So, in this case a decent sample size is generated having a positive effect on the generalizability. Whereas the
26 reliability of the study is improved since the sampling frame is clearly identified making it easier to copy the research.
4.3 Data collection
This study uses secondary data to answer the research question by making use of online DJ databases, social media and music platforms disclosing online DJ status, popularity and genres. Also, a category spanning system is being developed in order to indicate the level of category spanning a DJ executes. This system is named as the genre classification system as it is related to music genres. Collecting the data was easy since the websites were easily
accessible, making it a convenient way of data collection. All the data was incorporated in a dataset for further analyses consisting of severalonline sources.
4.3.1 DJ databases
The category spanning system is composed by data retrieved from four individual online sources, namely: Top Deejays, DJ Rankings, The DJ List and Partyflock. In order to measure DJ status, Rankingz is being used to retrieve the data. At last, Discogs is used to determine the years of experience of a DJ. The different databases will be briefly discussed below.
Top Deejays
Top Deejays is an online DJ database which is founded in Slovenia. The website provides a ranking based on an algorithm generated by combining a DJ’s the number followers and subscribers on Facebook, Twitter, You Tube, Soundcloud, MySpace and Last.fm . The website enables the visitor to filter the DJs by genre, country or social network. Furthermore,
27 DJ profiles can be visited and created containing statistics, associated artists and the
categorization of the DJ genre. (TopDeejays, 2016).
DJ Rankings
DJ Rankings is an originally Japanese online DJ community which was established in 2012. They calculate their DJ ranking by making use of an algorithm which considers the following data and information; DJ fees and salaries, ranking preferences from their own DJ
community, media presence and fame, chart data from remixes and music releases, airplay data from radio stations, amount of royals collected, followers on social media like Facebook and Twitter, polling and rating data from sites like DJ Mag. As a final judgement, DJ
Rankings’ expert jury team reviews the results and makes adjustments taking a DJ’s skills and craftsmanship into consideration. The list consists of a DJ’s country of origin, number of ranking and its genre. DJs can be filtered by country and genre. Also, the website runs its own DJ polls and remix competitions (DJ-Rankings, 2016).
The DJ List
The DJ list is a USA based DJ directory since 1997, which provides news, videos, upcoming shows and global rankings. The database represents over 500,000 EDM DJs and nearly 900,000 thousand registered users. They offer customized dance music content based on the interest of every consumer. Members receive notifications when their favourite artists release new music, announce upcoming shows, news, interviews and events. The website enables the visitor to filter the DJs by country or genre. Furthermore, the ranking per genre is being displayed same as global ranking. Also the website proposes related artists which are associated with the DJs a member follows (TheDJList, 2016).
28
Partyflock
Partyflock is online Dutch dance community found in 2001. Visitors can register a profile enabling them to add dance events to their agenda, pictures and friends. Also someone can customize the input of the website by updating its favourite genres. The website displays upcoming and past events filtered by artist, city, venue or organization. Furthermore, Partyflock has a forum where users can communicate and share their thoughts about music and events. The website also offers to watch footage of past events, artist interviews and music reviews. When searching for particular artists, a biography is presented same as its genre and other personal information (Partyflock, 2014).
Rankingz
Is an online platform which calculates the ranking of status of different categories by offering a Reputation Performance Analysis (RPA). This RPA platform visualizes the day-to-day performance of a status provided by data generated by the services and users of the internet. They monitor search engines, blogs, for a, wiki’s, micro blogs, communities and social networks. On the website the visitor is able to choose among different categories to show the status rankings of brands or producers. For instance, a ranking list of the online status of DJs can be found in the music directory displaying up to 429 DJs. It displays the rank of DJs according to its online status together with the amount of followers on Twitter and Facebook. Also the average buzz surrounding a brand is being measured same as the influence of the brand expressed in the ‘Klout’ score. A complex algorithm then calculates the online status (Rankingz, 2015).
29
Discogs
Discogs is the largest user generated marketplace and database focussing mainly on electronic music. More than 239,000 people have contributed in some extent by adding input and
content to build up a catalog of more than 7.3 million recordings and 4.5 million artists from almost 750,000 music labels. The database can be filtered by genre, format (e.g. CD, album, vinyl), decade or county. The profile of artists contain a biography and a discography which is a comprehensive list of recordings made by a particular artist. It also shows links to the
marketplace where related vinyl and CDs can be purchased and sold. Furthermore, the
website offers a community and forum where music and other related topics can be discussed (Discogs, 2016).
4.3.2 Ranking lists and social media
In order to measure online DJ status, online platform named Rankingz makes use of social media sources Facebook and Twitter. Consumer appeal, in turn, is being measured by the DJ Mag ranking.
Facebook has over a billion of registered users, making it the world’s most popular online networking platform. Users are able to create their own profile including personal
information, interests and footage. They can engage with other profiles and post messages to show their interest to friends, colleagues, family or just a random person. Furthermore, the network enables the user to build their own list of friends with a customized news feed
30 adjusted to the user’s preferences and online behaviour. Also events and public profiles in line with your interests can be created.
Twitter is an online social network which connects 302 million active users on a monthly basis. On this platform, registered users can post messages known as tweets. Every day, 500 million tweets are being sent on Twitter (Twitter, 2016). These tweets have a maximum of up to 140 characters and the most recent tweets by someone are shown on top in his user’s profile. Every personal account has the symbol “@” in front of the name, which enables other users to tag a particular user in a tweet by putting “@” in front of the person’s user name. To mark keywords, the symbol “#” is preceded by the particular word. This hashtag assigns the tweet to a particular topic and by clicking on one, the user is being directed to a complete list of all tweets containing that particular hashtag or topic. Accordingly, a tweet could consist of several hashtags. Furthermore, users can follow each other, which enables them to keep track of the tweets the particular person posts provided that these tweets are not set to be private. One of the most important feature of Twitter is that a tweet can be retweeted, entailing that the original tweet is being copied onto the user’s profile. This retweet will then be visible for the user’s followers, making it a valuable tool for generating electronic word-of-mouth for brands or producers (Jansen, Zhang, Sobel & Chowdury, 2009).
31
DJ Mag
The DJ Mag, fully named DJ Magazine, is a dance music related magazine founded in the UK in 1991. The website attempts to replicate much of the content of the magazine which
contains DJ interviews, reviews and EDM related news. On an annual basis, DJ Mag discloses a ranking for clubs and DJs based on public votes. The first DJ Mag top 100 poll was
introduced in 1995 (DJ Mag, 2010). Recently, DJ Mag has suffered from critics questioning its accuracy as a measure for the number one DJ of the world. This is because the list measures the extent of appeal of the DJ in eyes of the audience instead of its skills and
performance. However, the DJ Mag top 100 is still the world’s most recognizable ranking list serving as a benchmark to third parties like clubs and booking agencies (EVAR, 2012).
4.4 Genre classification system
According to DiMaggio (1987), artistic categorization systems are likely to differ across societies. This was confirmed by the genres that widely differed across the previously stated databases. The genre classification system was composed in order to accurately measure category spanning. This system is composed out of four databases, namely: (1) Top Deejays, (2) DJ Rankings, (3) The DJ List, and (4) Partyflock (Appendix B).
At first, the various categorization methods used on the different databases where determined and whether it is updated on a regular basis. Emails were send to the databases in order to assure legitimacy. It turned out that only crew members of Top Deejays replied to confirm that their website was up-to-date. Furthermore, it was notified that the genres of the DJs where adjusted in case a DJ requested it. Also the DJ profiles, that are generated by Top Deejays’ crew members, are validated by comparing them with other social media websites. Since no representative of the other three databases replied, the construction of the genre
32 classification system was assisted by EDM experts with over 10 years of working experience in the EDM scene. In this way, the validity and reliability of the process could be maintained. Then the data needed to be filtered to make it consistent. Firstly, the misspelled genres were aligned, for instance ‘psy-trance’ and ‘psychedelic trance’ where rephrased to one genre. As a result, the number of genres were decreased to a number of 46. An overview of the rephrased genres is shown below in Table 1.
Table 1: Modifications in Genre Classification System based on name similarity
Source Original classification New classification
Top Deejays Breaks Breakbeat
Hard Techno Hardcore / Hard techno
Psy-trance Psychedelic trance
DJ Rankings Hardcore techno Hardcore / Hard techno
Indie / Underground Indie dance Traditional house House
The DJ List Indie dance / Nu disco Indie dance
Psy-trance Psychedelic trance
Partyflock Deephouse Deep house
Electro Electro house
Hardcore Hardcore/ Hard techno
Progressive Progressive house
33 After that, the categorization system was filtered by checking whether genres were represented on at least two online sources. Since the four databases originate from four different countries (e.g. Slovenia, Japan, United States and the Netherlands), it was assumed that there would be an international consensus to some extent when a genre was presented on two or more of them. Consequently, 16 genres met the requirements as shown in Table 2. The genres surviving the cut were in alphabetical order: breakbeat, deep house, drum & bass, dubstep, electro house, electronica, hard dance, hardcore / hard techno, house, indie dance, minimal, progressive house, psychedelic trance, tech house, techno and trance.
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Table 2: Genre representation across the four databases
Genre Number of sources Genre 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 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
35 Afterwards it was assessed whether the remaining 30 genres could be assigned to any of the 16 main genres in collaboration with the EDM experts. Accordingly, eight changes were made on behalf of the experts. The assignation of the sub genres to main genres is being presented in Table 3. As a remainder, 22 sub genres could not be categorized as one of the 16 main genres. These leftovers are: 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 at last Urban.
The genre classification system failed to categorize the leftovers mainly due to three reasons. In the first place, some of the genres shared characteristics of several other genres. For instance, ‘Hardhouse’ sharing characteristics with ‘Hard dance’ but also ‘House’. Secondly, some of the leftovers represented a complete different genre which did not share any similarities with one of the main genres, for instance ‘Jungle’ and ‘Acid’. Finally, several genres like ‘Funk’, ‘Latin’ and ‘Hiphop’ were not seen to be classified as Electronic Dance Music as overarching category and are therefore excluded.
Table 3: Adjustments in Genre Classification System due to 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
36 4.5 Variables and Measures
Independent variable
The independent variable used in this study is category spanning. Category spanning for DJs is measured by making use of the Genre Classification System which have been conducted with help of two expert with over ten years of experience in the EDM industry. This system calculates genre spanning by adding up the distinct number of genres associated with every DJ across the four online DJ databases (e.g. Top Deejays, The DJ List, DJ Rankings and Partyflock). For instance, when a DJ is classified as ‘Dubstep’, ‘Drum & bass’ and ‘Progressive house’, the value of category is set at three.
Moderating variable
The moderating variable is status. This online DJ status is being measured by Rankingz, an online ranking platform making use of advanced algorithms keeping track of the data daily and is being updated every week. In order to make a comparison with the online reputation of peers, The Reputation Performance Analysis (RPA) measures the DJ’s influence represented in the ‘Kloud’ score. This is done by tracking the development of his fan base, the
development of buzz around a brand or the share of voice of the DJ within the group of peers. This data is gathered by monitoring search engines, blogs, wiki’s, micro blogs, communities and social networks, more insight is provided into the following things:
1. The presence, reach or awareness of the DJ: measures how ‘big’ the brand is online, based measuring its fan base on various channels like Facebook and Twitter.
2. The activity: measures how active the DJ is, for instance online posts on social media. Also activity concerning the DJ is measured so for instance releasing a new song or the announcement of a new show.
37 3. Engagement: measures how many active fans are mentioning the DJ’s name in
messages and how frequently
Based on these three measures an advanced algorithm calculates the online status of a brand or in this case a DJ.
The website itself states it measures the online reputation of a brand or producer. However, as stated before in the literature, Washington and Zajac (2005) argue that reputation entails differences in quality or merit generating rewards which are specifically based on performance whereas status describes the differences in agreed-on social ranks which generate privileges. Thereby, Ertug and Castellucci (2013) have shown that an ordering in reputation can be determined by an actor’s past performance. Instead of measuring a DJs performance, Rankingz focusses more on judgement of social rank based on hierarchy of values which are the three measures presented previously. Therefore, this study considers Rankingz to be measuring a DJ’s status rather than its reputation and is considered to be an objective source as no individual assessment is being integrated which could lead to bias (Saunders et al., 2011). Moreover, the DJ status data is retrieved from Rankingz at June 15, 2016. In terms of computing the variable, status is an ordinal variable, encompassing that number one has a higher status than number two. To make the results easier to interpret, this variable will be calculated inversely. Rt = [1, 187], a DJ's inverse listing (188 - Rt), this entails that the DJ with the lowest status will be indicated with a one.
Dependent variable
In this study consumer appeal is considered as the dependent variable. Consumer appeal is being measured according to the DJ Mag top 150 from 2015 which has been announced in
38 October 2015. This poll is based on consumer votes which makes it a good representation of how appealing a DJ is in the eyes of the audience. Since status is slowly rendered by change (Ertug & Castellucci, 2013), it is assumed that the data retrieved from Rankingz in june 2016 has not changed that much in respect to the DJ status in October 2015 which is when the DJ Mag was announced. Same as for status, this variable is based on ordinal ranks. To make it easier to interpret the results later on, this variable will be computed inversely. Rt = [1, 413], a DJ's inverse listing (414 - Rt), implying that a higher rank indicates a higher popularity.
Control variables
One of the control variables used in this study is a DJ’s years of experience. The data representing this variable is retrieved from online data base Discogs. Years of experience is calculated by taking the year the artist entered the industry, which is the year of the first release of an artist’s recorded music (Mattsson et al., 2010) and subtract this from the current year, which is 2016. The artist entry is the first registered release, which could either be an album or a single. It is expected that years of experience has an influence on consumer appeal as in most cases DJs need to establish their selves before getting recognized. Furthermore, the second control variable used in this study is age. It is interesting to see whether the age of a DJ has an effect on the relationship of category spanning on consumer appeal. Maybe the audience find younger DJs more appealing than older ones.
39 5. Results
The research design and methods used in this research were discussed in the previous section. This paragraph will illustrate the results of the research which starts with some descriptive statistics, then reliability measures will be presented. After that, the results regarding the regression analysis will be addressed.
5.1 Descriptive statistics
At first, the data consisted of 370 DJs. However, the majority did not meet the criteria of being present in at least two or three out of the four databases which led to a sample of 187 DJs. From this sample, not every DJ was listed in the DJ Mag list as this ends at 150. Also not every DJ was listed in the Rankingz list which displays 429 DJs. As a consequence, these cases were excluded which ultimately resulted in a final sample size of 103 (N = 103). The sample consists of 103 DJs from 22 different countries with only 2 of them being female. Which shows the dominance of male DJs in EDM as advocated by McLeod (2001). Martin Garrix was the youngest DJ in the sample with an age of 20 and Carl Cox the oldest with an age of 53. The age distribution of the DJs is shown below in Figure 1. It shows that 50 percent of the DJ’s age varied between 28 and 38 with a mean of 33. The skewness is .519implying that the distribution is moderately positively skewed and the kurtosis is almost zero indicating that the distribution is quite flat. In this sample, most DJs are Dutch (n = 34) as the distribution shows in Figure 1. This is followed by the United States (n = 13), the United Kingdom (n = 9), Sweden (n = 8) and Germany (n = 6). These 5 countries account for 67.96% of the DJs in the sample. A complete overview is being presented below in Figure 2.
40
Figure 1: Boxplot displaying age
Figure 2: Histogram showing distribution of DJs per country
1 1 5 1 3 1 4 6 2 4 1 1 34 2 1 3 1 8 1 1 9 13 0 5 10 15 20 25 30 35 40
DJs per country
41
5.1.1 Sample characteristics
Genre distribution
Below in Figure 3 the distribution of genres across the various databases the same is being displayed regarding the sample. It turned out that progressive house is the most popular genre among the DJs in the sample accounting for 140 classifications. This is followed by electro
house, trance, house and hard dance. These five genres represent 74.5% of the overall genre
representation in the sample. Additionally, breakbeat is the most underrepresented genre with only 1 classification.
Figure 3: Genre distribution and percentage per source
1 (.2%) 2 (.3%) 16 (2.8%) 22 (3.8%) 127 (19.9%) 9 (1.5%) 35 (5.7%) 28 (4.6%) 81 (12.9%) 5 (.8% 3 (.5%) 140 (22%) 6 (1%) 10 (1.6%) 24 (4.2%) 87 (14%) 19 (3%) 0 20 40 60 80 100 120 140 160 Breakbeat Deep house Drum & bass Dubstep Electro house Electronica Hard dance Hardcore / Hard techno House Indie Dance Minimal Progressive house Psychedelic trance Tech house Techno Trance Other
Genre distribution
42
Category spanning
The extent of category spanning varied across the four databases used for categorizing the DJs. When looking at DJ Rankings (n = 99), the majority was identified to be in only one single category (n = 72). The remaining 27 DJs were associated with two genres (M = 1.27,
SD = .45) The total number of DJs categorized in the DJ Rankings does not equal the total
sample amount (n = 103), this is due to that fact that some DJs are not categorized by every database. However, they still meet the criteria that every DJ needs to be represented in at least two out of the 4 databases in order to be integrated in the sample. For TopDeejays (n = 102) the majority was categorized in two genres (n = 93) and only 8 were associated with one genre (M = 1.90, SD = .33). Partyflock (n = 94), in turn, spanned categories between five (n = 2) and one (n = 34) with an increasing frequency in decreasing order and illustrated one outlier of 9 (M = 2.17, SD = 1.26). Similar to DJ Rankings, The DJ List (n = 95) has the majority of DJs categorized in one genre (n = 56) and 39 in two genres (M = 1.3, SD = .61). This shows that the distribution of DJ Rankings and The DJ List is in contrast to TopDeejays who categorize most DJs to be in two genres (n = 93). The complete overview regarding the distribution of the categories across the databases is presented in Figure 4.
43
Figure 4: Category spanning distribution
Years of experience
The DJs in the sample (N = 103) all have varying years of experience in the EDM industry. It differed from 2 to 29 years (M = 10.46, SD = 5.8), as displayed below in Figure 4.
Furthermore, it turned out that years of experience was not normally distributed showing a positive skewness of 0.762 (SD = .24) and kurtosis of -0.201 (SD = .47). Additionally, the Shapiro-Wilk test showed the significance (p < .001) of years of experience which also shows the non-normality of the variable.
72 27 56 39 8 93 34 26 25 6 2 1 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 9 Fre q u en cy Number of categories
Category spanning
44
Figure 4: Boxplot displaying years of experience
5.2 Correlations
Prior to testing the hypotheses, some correlations will be explored between the variables. These correlations are being displayed in Table 4 and show some interesting findings. It seems that category spanning is almost not correlating with consumer appeal (r = 0.059) and highly insignificant (p = .554). This is surprising since according to Hsu (2006), Negro and colleagues (2010) a negative relation should be the case. Therefore, this outcome is contrary to this study’s expectations. Furthermore, DJ status and consumer appeal are moderately correlating (r = .491) and very significant (p = .000). This is in line with the study’s
expectations that status serves as some signal of quality in the eyes of the audience leading to higher appraisal (Podolny, 1993). Furthermore, years of experience and consumer appeal are slightly negatively correlated (r = -.033) and far from significant (p = .740). This might be
45 due to that the audience could perceive someone as outdated when working too long in the industry. Additionally, control variable age displays a low negative correlation with consumer appeal (r = -.171, p = 0.084) indicating that once a DJ gets older popularity and appeal seems to decline. Category spanning and age seem to have a low correlation as .00 < r < .30 though significantly (r = .279, p = .004). Maybe this is the case because once a DJ gets older he tends to explore new genres and music style as his DJ career evolves and gets shaped along the way. A DJ’s age is highly positively correlating with years of experience, which lies in the line of expectations (r = .785, p < 0.01) since a DJ who gets older also gains a year of extra experience in the scene. Also, age has a low negative correlation with consumer appeal (r = -.171, p = 0.084) indicating that once a DJ gets older popularity and appeal seems to decline. Thereby, category spanning correlates significantly with DJ status but is identified to have low positive correlation (r = .291, p = .003). This is low positive correlation is also occurring between category spanning and years of experience (r = .217, p = .028). These correlations could mean that DJs with a particular status or experience in the industry have already
established their selves in some way in the eyes of the audience and may therefore experience more freedom to span categories. On the same time, years of experience and DJ status are correlating significantly but also low (r = .219, p = .026). This makes sense as in most cases it takes some time to establish a particular status (Lynn, Podolny & Tao, 2009).
46
Table 4: Mean, Standard deviation and Pearson correlations of variables
M SD 1 2 3 4 1. Category spanning 1.68 .40 2. DJ status 328.49 69.08 .291** 3. Age 33.17 7.02 .279** .115 4. Years of experience 10.46 5.80 .217* .219* .785** 5. Consumer appeal 87.95 43.25 .059 .491** -.171 -.033 Note: N=103, *p<0.05, **p<0.01 (2-tailed) 5.3 Regression analyses
5.3.1 Category spanning and Consumer appeal
At first, it was hypothesized that category spanning has a negative effect on consumer appeal. To investigate the relationship between category spanning and consumer appeal, a
hierarchical regression analysis is executed. By making use of a hierarchical analysis, the extent of predictive power of category spanning for consumer appeal can be examined. This can be done after controlling for age and years of experience. In this way, the shared
variability of these variables can be controlled within the predictive variable category
spanning. The overview of the results of the hierarchical analysis is being presented below in
Table 5. Step 1 shows that the model was not statistically significant F (2, 100) = 2.968; p >
0.05 and explained 5.6% of the variance in consumer appeal. After the entry of category spanning at Step 2, the total variance the total variance explained by the model as a whole was equal to 6.9% F (3, 99) = 0.013; p > 0.05. The introduction of category spanning into the model explained an additional 1.3% of the variance in consumer appeal, after controlling for age and years of experience (R ² Change = .012; F (1, 99) = 1.328; p > 0.05). In the final
47 model, one out of four turned out to be significant which is age (β = -.412, p < 0.05). It is striking to see that in the final model control variable named years of experience have a higher beta than category spanning. However, category spanning has a higher beta than age as this value is negative (β = -.412). Thus, the relationship between category spanning and consumer appeal is not significant which suggests hypothesis 1 is rejected.
Table 5: Hierarchical regression model of Consumer Appeal
R R ² R ² Change B SE β T Step 1 .237 .056 Age -2.331 .966 -.378 -2.412 Years of experience 1.969 1.170 .264 1.684 Step 2 .262 .069 .012 Age -2.535 .981 -.412* -2.584 Years of experience 1.974 1.168 .265 1.691 Category spanning 12.679 11.001 .116 1.153
Note; Statistical significance: *p<.05; **p<.01; ***p<.001
5.3.2 Regression analysis with moderator included
The hypothesis which is tested here says that status of DJs will negatively affect the negative relationship of category spanning on consumer appeal. To investigate this moderating effect, a regression analysis is executed. To avoid potentially high multicollinearity with the
interaction term, the variables were centered before creating an interaction variable between category spanning and DJ status (Aiken & West, 1991). The analysis was executed by doing a regression analysis of the direct effect between category spanning and consumer appeal plus between status and consumer appeal. After this the interaction effect (category spanning
