WAS ON-DEMAND MUSIC STREAMING A

DISRUPTIVE INNOVATION?

 

by

Dean Diehl

 

Dissertation

 

Submitted to the Faculty of

Trevecca Nazarene University

School of Graduate and Continuing Studies

in Partial Fulfillment of the Requirements for

the Degree of

 

Doctor of Education

In

Leadership and Professional Practice

 

May 2019

 

 

 

WAS ON-DEMAND MUSIC STREAMING A

DISRUPTIVE INNOVATION?

 

by

Dean Diehl

 

Dissertation

 

 

 

 

 

__________________________________ _______________ Dissertation Adviser Date

 

___________________________________ __________________

Dissertation Reader Date

 

___________________________________ __________________

Dissertation Coordinator Date

 

___________________________________ __________________

EdD Program Director Date

 

___________________________________ __________________

Dean, School of Graduate & Continuing Studies Date

02/26/2019

02/26/2019

02/26/2019

02/26/2019

02/26/2019

 

 

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© 2019

Dean Diehl

All Rights Reserved

 

 

 

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ACKNOWLEDGEMENTS

I would like to thank my dissertation advisor, Dr. Jea Agee, for his invaluable

assistance in completing this project as well as Dr. Randy Carden, Dr. Glenn Schmidt,

and Dr. Tim Brown for their input and guidance. I would also like to thank Dr. Jim Hiatt,

Dean of the Skinner School of Business and Technology as well as Greg Runyan,

Chairman of the Skinner School of Business and Technology for their encouragement

and accommodation as I completed this work.

 

 

 

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ABSTRACT

by

Dean Diehl, Ed.D.

Trevecca Nazarene University

August 2019

Major Area: Leadership and Professional Practice Number of Words 106

Disruptive innovation theory, introduced and developed by Dr. Clayton Christensen in

the late 1990s, has come to be confused with any innovation that encroaches upon

existing options. In order to clarify the theory of disruptive innovations, scholars have

repeatedly called for the application of the core concepts of the theory to the data

surrounding the introduction of innovations from various fields. This study applied the

concepts of disruptive innovation theory to the data surrounding the introduction and rise

of on-demand music streaming between the years of 2001 and 2017 in order to test

whether on-demand music streaming constituted a disruptive innovation as defined by the

theory.

 

 

 

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TABLE OF CONTENTS

I. INTRODUCTION ………………………………………………………………………………………….. 1

Statement of the Problem ………………………………………………………………………………… 3

Rationale ………………………………………………………………………………………………………. 4

Research Questions ………………………………………………………………………………………. 13

Contribution of the Study ……………………………………………………………………………… 15

Process to Accomplish ………………………………………………………………………………….. 16

II. REVIEW OF THE LITERATURE …………………………………………………………………. 21

Historical Perspective …………………………………………………………………………………… 23

Digital Downloads: A Sustaining Innovation …………………………………………………… 42

Conclusion ………………………………………………………………………………………………….. 46

III. METHODOLOGY ……………………………………………………………………………………….. 47

Research Design ………………………………………………………………………………………….. 49

Participants …………………………………………………………………………………………………. 52

Data Collection ……………………………………………………………………………………………. 55

Analytical Methods ………………………………………………………………………………………. 58

IV. FINDINGS AND CONCLUSIONS ………………………………………………………………… 63

Findings ……………………………………………………………………………………………………… 64

 

 

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Summary of Findings …………………………………………………………………………………… 83

Limitations ………………………………………………………………………………………………….. 87

Implications and Recommendations ……………………………………………………………….. 90

 

 

 

 

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LIST OF TABLES AND FIGURES

Figure 1.1 Innovation in Video Software (Shapriro, 2014). ……………………………………….. 6

Figure 2.1 Diffusion of innovations over time and by frequency (Rogers, 2006). ……….. 25

Table 3.1 US Music Consumers (MusicWatch, 2018). ……………………………………………. 53

Table 3.2 US Raw Sales Data for the First Two Weeks of 2017 (Nielsen, 2018). ……….. 56

Table 3.3 US Converted Data for First Two Weeks of 2017 (Nielsen, 2018). …………….. 57

Table 4.1 Playback Media Performance ………………………………………………………………… 65

Table 4.2 2008 Total Music Consumption by Format (Nielsen, 2018). ……………………… 69

Figure 4.1 2008-2010 Weekly US Consumption by Format (Nielsen, 2018). …………….. 70

Table 4.3 2008-2010 Correlation between CDs, DL Albums, DL Songs, and Streams

(Nielsen, 2018). …………………………………………………………………………………………… 71

Table 4.4 2008-2010 Average Consumption by Format (Nielsen, 2018). …………………… 73

Table 4.5 2008-2010 Paid-to-Non-Paid Ratio (Nielsen, 2018). ………………………………… 74

Table 4.6 2008-2010 Average % of Consumption in Streaming (Nielsen, 2018). ……….. 75

Figure 4.2 2011-2017 Weekly US Consumption by Format (Nielsen, 2018). …………….. 81

Table 4.7 2011-2017 Correlation between CDs, DL Albums, DL Songs, and Streams

(Nielsen, 2018). …………………………………………………………………………………………… 82

Figure 4.3 2008-2017 Weekly US Consumption by Format (Nielsen, 2018). …………….. 86

Table 4.8 Growth in Streaming % of Total by Genre from 2011-2017 (Nielsen, 2018). . 91

 

 

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CHAPTER ONE

INTRODUCTION

Creative Destruction is the essential fact about capitalism.—Joseph A. Schumpeter

All innovation is disruptive. Not every innovation, however, is a disruptive

innovation properly understood (Schmidt & Druehl, 2008). Confusion over what

constitutes a true disruptive innovation has led many leaders to make tactical and

strategic business errors, often with tragic results (Christensen, Raynor, & McDonald,

2015). Christensen et al. (2015) stated, “The problem with conflating a disruptive

innovation with any breakthrough that changes an industry’s competitive patterns is that

different types of innovation require different strategic approaches” (p. 4). Leaders must

learn to distinguish true disruptive innovation from other forms of innovation.

Simply stated, a disruptive innovation is one in which the innovation’s initial

performance is considered to be inferior to existing options in those attributes most

valued by the mainstream market, called core competitive dimensions, leading

mainstream consumers to dismiss the innovation. A disruptive innovation, however,

survives because it finds a place among low-end consumers of the existing market or

creates a new market due to its unique business model or its superiority to existing

options in one or more attributes, called secondary competitive dimensions. Over time,

the innovation improves its performance in the core competitive dimensions while

maintaining its unique advantages until it becomes acceptable to the mainstream,

 

 

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allowing the innovation to encroach upon or disrupt existing options thus shifting the

competitive landscape (Christensen, 1997; Schmidt & Druehl, 2008).

Largely originating with the Clayton Christensen book, The Innovator’s Dilemma

(Christensen, 1997), and refined over the last two decades, disruptive innovation theory

generated much praise and more than a little criticism. Danneels (2004) stated, “One can

see from a search for disruptive technology on the web how loosely the term has come to

be used and how it has become separated from its theoretical base” (p. 257). Even

Christensen et al. (2015) agreed, stating, “Despite broad dissemination, the theory’s core

concepts have been widely misunderstood and its basic tenets frequently misapplied” (p.

4).

Properly applying a theory strengthens the theory. Scholars writing about

disruptive innovations have been consistent in pointing out the need for additional

involvement from both academics and practitioners in the process of identifying and

clarifying the key characteristics of disruptive innovations (Christensen et al., 2015;

Danneels, 2004; Schmidt & Druehl, 2008). It is particularly important to study industries

not previously examined in order to establish those characteristics of disruptive

innovations with broad applicability versus industry-specific characteristics (Danneels,

2004).

Clarifying and demonstrating the essential characteristics of disruptive

innovations is necessary to arriving at a predictive model of disruption. As Danneels

(2004) put it, “The real challenge to any theory…is how it performs predictively” (p.

250). Christensen et al. (2015) concurred stating, “As an ever-growing community of

researchers and practitioners continues to build on disruption theory and integrate it with

 

 

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other perspectives, we will come to an even better understanding of what helps firms

innovate successfully” (p. 11).

With that context in mind, one innovation that bears examining is on-demand

music streaming. On the surface, the history of on-demand music streaming followed the

pattern of a disruptive innovation. However, while the popular press has covered

streaming in the music industry extensively, few scholarly articles exist, and, most of

what does exist relied on incomplete summary data available to the public. An in-depth

analysis of on-demand music streaming supported by comprehensive data from inside the

industry is the kind of study called for by disruption scholars in the hope of further

refining the theory of disruptive innovations.

Statement of the Problem

The purpose of this study was to apply disruptive innovation theory to data

surrounding the introduction and rise of on-demand music streaming in the United States.

Through the collection and analysis of quantitative and qualitative data in the form of

archival sales records and documents, this study considered whether on-demand music

streaming possessed the essential characteristics of a disruptive innovation as defined by

the theory. Conducted in response to a call from disruption theorists for the application of

disruptive innovation theory to industries and innovations not previously studied, this

study attempted to identify patterns and uncover anomalies that would strengthen the

theory.

According to the theory, for on-demand streaming of music in the United States to

have been a true disruptive innovation, it would have initially been inferior to existing

options in a core competitive dimension. As a result, mainstream consumers of music

 

 

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would have rejected on-demand music streaming. In spite of this rejection, on-demand

music streaming would have appealed to the low-end of the market or established a brand

new market through its unique business model or through its superior performance in

some secondary competitive dimension. Finally, over time on-demand music streaming

would have improved performance in the core competitive dimension until it became

acceptable to mainstream consumers leading to the disruption of existing options and a

shift in the overall competitive landscape of music (Christensen, 2015; Schmidt &

Druehl, 2008). It was the goal of this study to test the facts of on-demand music

streaming against these essential elements of a disruptive innovation.

Rationale

Disruptive innovation theory has been disrupted. Twenty years after first

introducing the concept of disruptive innovations, initially called disruptive technologies,

Clayton Christensen summed up the current state of the theory in a 2015 Harvard

Business Review article titled, “What is Disruptive Innovation” (Christensen, Raynor &

McDonald, 2015) where he commented, “Disruption theory is in danger of becoming a

victim of its own success.” Christensen went on to say, “In our experience, too many

people who speak of ‘disruption’ have not read a serious book or article on the subject”

(Christensen et al., 2015, p. 4).

Disruptive innovation theory has been criticized as too narrow (Downes & Nunes,

2013), too broad (Danneels, 2004), and even outdated (Wessell, 2016). There have been

calls for clearer definitions and categorizations (Schmidt & Druehl, 2008) as well as calls

for broadening the definitions (Wessell, 2016). It is safe to say disruptive innovation is a

 

 

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theory in need of further testing of its core concepts against real-world innovations to

define just what a disruptive innovation is, and what it is not.

Disruptive innovations theory is an offshoot of diffusion of innovations theory, a

theory that goes back to the early 1960s with roots in sociology, psychology, and

marketing. Often associated with the work of Everett Rogers (2003), diffusion of

innovations theory deals with the way new ideas and products move, or diffuse, through a

community. Rogers (2003), in summarizing the concept, wrote, “Diffusion is the process

by which 1) an innovation 2) is communicated through certain channels 3) over time 4)

among members of a social system” (p. 11., emphasis in original).

In diffusion, a key concept to understand is compatibility, or “the degree to which

an innovation is perceived as consistent with the existing values, past experiences, and

needs of potential adopters” (Rogers, 2003, p. 240). Opinion leaders, the key influencers

within an industry’s market, look for innovations that are, as Valente (2006) put it,

“compatible with the culture of the community” (p. 68). Innovations perceived as

incompatible are often delayed or rejected by opinion leaders (Valente, 2006). Therefore,

dependence on adoption by opinion leaders within an industry causes innovation within

that industry to concentrate on a desired attribute or set of attributes called core

competitive dimensions (Christensen, 1997).

Successful firms within an industry anticipate the peak performance of existing

options and introduce successor innovations accordingly. These successor products or

services innovate along the core competitive dimension with each product outperforming

the previous product in that dimension (Christensen, 1997). Over time, succeeding

 

 

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innovations produce an upward rising performance curve along the core competitive

dimension (See Figure 1.1).

 

 

Figure 1.1 Innovation in Video Software (Shapiro, 2014).

 

Using an illustration from the film industry, innovation in video software

developed along the core competitive dimension of portability, referring to the ability to

take your movies with you. 16 mm film, with its clunky projectors, large reels, and need

for a screen were not very portable. The VHS cassette provided much more portability

and the DVD, with its thin, durable disc, was even more portable than the VHS (Shapiro,

2014).

From the 16 mm film to the DVD, existing firms and content owners within the

film industry, motivated by the needs of their core consumers, drove innovation towards

ever-increasing portability (Shapiro, 2014). Christensen (1997) defined this type of

innovation as sustaining innovation. Christensen et al. (2015) stated that sustaining

innovations “make good products better in the eyes of one’s existing customers” (p. 5).

 

 

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Where disruptive innovations depart from sustaining innovations is that they are

initially inferior in regards to performance in the core competitive dimension, innovating

instead along some new or overlooked secondary competitive dimension or through the

development of a unique business model (Danneels, 2004; Schmidt & Druehl, 2008).

This inferiority in regards to performance in the core competitive dimension causes

opinion leaders to reject the innovation (Schmidt & Druehl, 2008). Ignored and rejected

by the mainstream, these innovations still manage to survive. Schmidt and Druehl (2008)

elaborate, “While existing high-end customers dislike the new product (they despise its

poor performance along the first dimension), a new market segment (or the existing low-

end segment) gladly accepts the de-rated performance along the first dimension in favor

of lower cost or the enhanced performance along the second dimension” (p. 352).

Disruptive innovations develop on the fringes of a market, or create a new market,

slowly evolving and improving performance over time. In the meantime, incumbent firms

innovating along the core dimension eventually overshoot the performance needs of the

market in the core dimension to the point that the market begins to shift their attention to

the previously undervalued secondary dimension or to the newly introduced business

model (Christensen, 1997). It is this shift in the entire basis of competition within a

market that is the hallmark of a disruptive innovation (Danneels, 2004).

When a disruptive innovation succeeds, it begins to take mainstream customers

away from incumbent firms, a process termed encroachment by Schmidt and Druehl

(2008). By the time incumbent firms realize what is happening, it is often too late to

respond. Before long, the disruptors have dominated the new market and the incumbents

are displaced (Christensen, 1997).

 

 

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For example, take the case of Netflix, the disruptive innovation that displaced

video rental stores such as Blockbuster. When Netflix first appeared in 1998, its mail-

based delivery system, built on the newly introduced DVD and a monthly subscription

model, had little appeal to mainstream video rental customers who largely rented VHS

tapes on impulse. However, with its unique monthly subscription business model and no

due dates, late fees, or shipping costs, Netflix appealed to a fringe market including early

adopters of DVD players, people who liked the convenience of ordering from home, and

video rental customers sick of exorbitant late fees (Auletta, 2014). Over time, Netflix

gradually and then increasingly encroached upon brick-and-mortar video rental stores.

Then, in 2007, when Netflix launched their on-demand streaming service, mainstream

video rental customers poured into Netflix to the degree that, by 2013, Blockbuster, the

largest video rental chain in the United States, declared bankruptcy (Christensen et al.,

2015).

The “innovator’s dilemma,” according to Christensen (1997) is that, based on

convention, ignoring innovations that are inferior in the core competitive dimension is the

right response. It made perfect sense for Blockbuster to ignore Netflix and focus instead

on convenience and selection, those dimensions most valued by their existing consumers.

Yet, as Christensen (1997) points out, this strategy often leads to disruption, or, in the

case of Blockbuster, bankruptcy.

In the wake of the publication of The Innovator’s Dilemma, much of the

discussion in the academic community centered on strategies for responding to disruptive

innovations when they appeared. However, because all innovation is to some greater or

lesser degree disruptive, there began to be a lot of misapplication of disruption theory,

 

 

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particularly among practitioners. Scott Anthony (2005) highlighted this new dilemma in

his article in the journal Strategy and Innovation, “Do You Really Know What You Are

Talking About?”:

The word disruption…has become loaded with meanings and

connotations at odds with the concept put forth by Clayton Christensen in

The Innovator’s Dilemma and highlighted in a 1999 Forbes magazine

cover story. As the term has increased in popularity, confusion about the

exact definition of disruption has increased as well, creating challenges for

companies seeking to grow through disruptive innovation.

Indeed, as the concept has seeped into the mainstream, this

language disconnect has generated confusion and led to the occasional

misallocation of resources. (p. 3, emphasis in original)

Anthony (2005) went on to state that confusion over what actually constitutes a

disruptive innovation is often due to three common mistakes: “1) mistaking disruptive

innovation for breakthrough innovation; 2) defining disruptive innovations against the

wrong parameters; and 3) forgetting that disruption involves more than technology” (p.

3). This confusion has led many to a call for further clarification of exactly what

constitutes a disruptive innovation (Danneels, 2004; Schmidt & Druehl, 2008). Schmidt

and Druehl stated, “(A) firm must be able to clearly delineate between what is a

disruptive innovation and what Christensen and Raynor (2003) and Christensen et al.

(2004) define as its converse: a sustaining innovation” (p. 347).

Disruptive innovation theory, like all theories, needs to be continually tested using

sets of historical data different from those already examined. As Danneels (2004) wrote,

 

 

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“(A) reconsideration of the nature of disruptive technological change and its

consequences for firms and industries is in order” (p. 257). Testing a theory provides

opportunity for anomalies not explained by the theory to emerge. Christensen (2006), in

an article on improving theories, stated, “The primary purpose of the deductive half of the

theory-building cycle is to seek anomalies, not to avoid them” (p. 45). It is by testing a

theory that the theory becomes stronger.

The rationale, therefore, for testing the theory of disruptive innovation against the

data surrounding the introduction and rise of on-demand music streaming within the

United States was to determine if the data aligned with the theory or if anomalies would

emerge. As stated previously, while music streaming in the United States has received

much coverage in the popular press, there is not much literature within the academic

community, due in part to a lack of access to the raw sales data necessary for industry-

level analysis. However, through a unique arrangement, The Nielsen Company, the

primary compiler and reporter of marketing information in the entertainment industry,

released complete historical sales data from 2008 through 2017 for the purpose of this

study, making industry-level analysis a possibility.

To set the context for the rest of this study, it is necessary to summarize the

history of online digital music. Online music services first appeared in the late 1990s,

almost exclusively through illegal file-sharing websites like Napster and Pirate Radio.

Because the great majority of early online music activity was illegal, it was hard to

measure the degree of disruption for existing music formats. Although there was much

speculation at the time as to the impact of illegal streaming on music purchases, lack of

reliable data made scientific inquiry impossible. In addition, what data there was came

 

 

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from a variety of sources with one source often contradicting another (Stevans &

Sessions, 2005). That said, all sources seemed to agree that illegal online activity

involved billions of downloads and streams (Auiar & Martens, 2013).

Researchers have taken every imaginable position as to the impact of illegal

activity upon legal options. Some claimed illegal activity killed legal purchases; others

posited there had been no impact at all because illegal users were never purchasers in the

first place; while still others stated the illegal activity actually increased legal purchases

of music (Stevans & Sessions, 2005; Auiar & Martens, 2013).

Regardless of the impact on sales, illegal downloading and streaming were not

without risks and inconveniences. Exposure to malware, viruses, and the potential

compromise of network security were all risks to file sharing. In addition, the activity was

illegal and thus subject to prosecution or penalty. Illegal music sites were also

cumbersome to use and often carried only a small portion of the titles available through

legal means (Machay, 2018).

In 2001, Apple, Inc. released the first iteration of iTunes, a music playback

software platform, initially only available for their own Macintosh computers, but, soon

after, available for all computer systems. In 2003, Apple released the iTunes digital music

store providing the first high profile, commercially viable, legal music download system

compatible with all major platforms. With licenses in place with practically every content

owner in the United States, the iTunes store gave consumers a legal way to purchase

digital files of the music they wanted (McElhearn, 2016). From 2001 through 2011,

digital purchases of albums and individual songs through iTunes and other sources such

as Google, Amazon, and Rhapsody, dominated online music activity, at least for those

 

 

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wishing to obtain digital music legally. In that period, Apple’s iTunes platform was the

clear leader with a market share of online music purchases that reached beyond 60%

(Bostic, 2013).

At about the same time as Apple was launching iTunes in 2001, Rhapsody, best

known for its desktop digital music player, launched the first legal on-demand streaming

platform (Evangelista, 2002). On-demand streaming differed from downloads in that,

instead of purchasing individual tracks and owning them, listeners paid a monthly fee for

access to a catalog of music. In effect, listeners were renting music versus owning it.

In its first iteration, Rhapsody offered subscribers access to thousands of songs,

many of which were from small independent labels, however, by 2002, Rhapsody had

licenses in place with all of the majors labels and offered over 175,000 songs for instant

on-demand streaming (Evangelista, 2002). While there was a fringe market interested in

the Rhapsody model, Apple’s iTunes dominated the digital music market leading Steve

Jobs to famously quote, “People have told us over and over and over again, they don’t

want to rent their music” (Ricker, 2015, para. 3).

In 2011, a new take on the streaming model emerged as Spotify, previously only

available in Europe, launched in the United States. Spotify was the first significant online

platform to provide free on-demand streaming. Using an ad-supported model, Spotify

offered consumers free access to over 15,000,000 songs. While there were significant

restrictions on the free version, Spotify’s subscription model offered a viable legal option

to listeners who were largely using illegal streaming services (Sisario, 2011).

From 2011 to the present day, on-demand streaming has experienced exponential

growth and paid billions of dollars in royalties to artists and content owners for activity

 

 

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that had largely been illegal and unpaid before the launch of Spotify. At the same time,

sales of physical formats and digital downloads have plummeted (Nielsen, 2018).

However, this does not automatically mean on-demand music streaming was a disruptive

innovation as defined by disruption theory (Christensen, 1997). As has been previously

stated in this chapter, disruption of previous activity within an industry does not

necessarily constitute a disruptive innovation. In order for on-demand music streaming to

have been a disruptive innovation, certain events must have occurred. The present study

addresses this issue in detail throughout the following pages.

Research Questions

The following questions were the focal point of the current study and formed the

overall process of investigation into whether or not on-demand music streaming

performed as a disruptive innovation as defined by current disruption theory.

1. Was there a core competitive dimension along which innovation occurred in

music playback formats prior to the introduction of on-demand streaming?

2. Was on-demand streaming initially inferior to existing music playback formats in

the core competitive dimension along which previous innovation had occurred?

3. Did the mainstream music market initially reject on-demand music streaming as a

music playback format?

4. Did on-demand music streaming find acceptance among the low-end consumers

of the existing market or create a new market due to its superior performance in a

secondary competitive dimension or through the introduction of a unique business

model?

 

 

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5. Did on-demand music streaming improve over time in the core competitive

dimension while maintaining its superiority in some secondary competitive

dimension or through its unique business model?

6. Did on-demand music streaming eventually encroach upon sales of existing music

playback formats resulting in a shift in the competitive landscape?

Description of Terms

Core Competitive Dimension. A core competitive dimension refers to the attribute

of a product or service most valued by the existing mainstream market of an industry

(Christensen, 1997).

Low-End Consumer. For the purpose of disruption theory, the term low-end refers

to “willingness to pay.” Low-end consumers, then, would be existing consumers with the

lowest price threshold (Schmidt & Druehl, 2008).

On-Demand Music Streaming. On-demand music streaming refers to streaming

platforms where the consumer may choose the exact song they wish to hear. On-demand

streaming is the only type of streaming included in music industry sales reporting

(Passman, 2015).

Portability. Portability refers to the degree to which a music platform allows

listeners to take their music with them (Gopinath & Stanyek, 2014).

Programmed Streaming. Programmed streaming refers to streaming platforms

where the consumer can only select the style of music they wish to hear, but cannot select

individual songs (Passman, 2015).

Secondary Competitive Dimension. A secondary competitive dimension refers to

an attribute of a product or service of less importance to the mainstream market of an

 

 

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industry but which has appeal to the low-end market or a new market for the product or

service (Christensen, 1997).

 

Contribution of the Study

Determining whether music streaming behaved as a disruptive innovation

according to current disruption theory benefitted three distinct populations: disruptive

innovation theorists, music distributors and content owners, and music business students.

Innovation theorists, including academics and practitioners, have been engaged in

an ongoing dialogue over the last twenty years, refining and adjusting the theory of

disruptive innovation. The goal of all of these endeavors has been to aid business leaders

in innovating successfully as well as in responding to disruption from others. While

views differ over many aspects of the theory, most theorists agree that analyzing data sets

from previously unexamined industries moves the theory forward by either confirming or

challenging its core principles. This study contributed to that effort.

An in-depth analysis of the data surrounding on-demand music streaming

identified consumer segments who were early adopters of the platform. The ability to

describe adopters of new music technology was of great benefit to music distributors and

content owners. First, it enabled the identification of consumers who have yet to adopt

streaming which can aid in future marketing efforts. Second, this study provided insight

into potential early adopters of future innovations in music distribution including better

understanding of those performance dimensions valued by the new opinion leaders.

Finally, relatively little data exists within academic literature regarding the

business side of music. This study codified and collected important data related to the

 

 

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introduction of music formats, the key competitive dimensions that have traditionally

shaped innovation in music distribution, and the introduction of music streaming. Having

these data appear in academic literature aided music business scholars looking for

background for future research.

Process to Accomplish

This study utilized a mixed-methods research design using quantitative data in the

form of archived sales records as well as qualitative data in the form of historical

documents, including press releases, news items, interviews, and journal articles. The

Nielsen Company, the primary collector and reporter of music sales information in the

United States, provided access to archived sales data under a special license for the

purpose of this study. The sales data included every legal transaction of music in the

United States from 2008 through 2017, including Compact Discs (CDs), digital

downloads, and on-demand streaming. Historical documents used in the study came from

library and public search engines, as well as proprietary documents available to the

researcher as an employee of Sony Music, Inc.

In many ways, this particular study was like a court case. Application of

disruption theory required a very specific sequence of events to occur (Christensen,

2006). This study divided those events into six research questions, each of which required

specific data. As a result, each question was its own miniature study requiring, in many

cases, its own set of data pulled from the various sources listed above.

The data provided by Nielsen was in the form of a massive website containing

data related to music transactions in the United States organized by artist, album, genre,

and format (CDs, digital downloads, on-demand streaming). The data used in this study

 

 

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included sales of CDs, digital downloads, and on-demand streams at the national and

genre levels for the years 2008 through 2017. Genres included in the study consisted of

Pop, R&B/Hip-Hop, Rock, Country, Latin, Christian/Gospel, Jazz, Dance/Electronic, and

World Music.

Qualitative data used in the study consisted of historical documents, including

press releases, news items, interviews, and journal articles from the period examined.

Because of the broad availability of reliable sources of archival material through the

internet, it has become more common for researchers to use historical data analysis as a

source of primary research (Fischer & Parmentier, 2010). One of the key components of

disruptive innovation theory has to do with consumer opinions and reactions at the time

of the introduction of the innovation (Schmidt & Druehl, 2008). Primary research in the

form of a new study would have required participants to recall how they initially felt

about an innovation introduced almost two decades ago. Archival documents from the

period, which captured the immediate impressions of consumers, the media, established

firms, and entrant firms at that time, provided a more reliable source of opinions, beliefs,

and reactions.

According to MusicWatch (2018), a marketing research and analysis firm focused

on the recording industry, the overall population of music listeners in the United States at

the time of the study consisted of 221 billion people, 55% of which were female and 45%

male. Whites made up 73% of the market, Blacks represented 13%, and other ethnicities

constituted the remaining 14%. MusicWatch tracked music consumption activity for

consumers aged 13 and older. Based on their research, 31% of music consumers were

between the age of 13 and 24. Consumers between 25 and 34 made up 28% of the market

 

 

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and those ages 35-44 were responsible for 18% of the market. The final 23% of the

market consisted of adults 45 and older. MusicWatch (2018) reported these estimates had

a +/- 1.75% margin of error. Because the information supplied by Nielsen for this study

was comprehensive of all music transactions in the United States, the quantitative data

used in this study encompassed the entire population of music listeners in the United

States as opposed to a sample.

Answering the first and second research questions, both of which examined the

performance of on-demand music streaming relative to existing formats in the various

competitive dimensions, required analysis of historical documents related to the features

of each of the primary music playback formats including LPs, cassettes, CDs, digital

downloads, and on-demand streaming. Documents analyzed included official press

releases from entrant firms, journal articles, and media reports from major trade and

consumer publications between 2001 and 2011. The researcher recorded, analyzed and

compared comments and opinions related to the various features and functions of each

format including portability, sound quality, depth-of-offering, price, and the overall

business model.

The third question, which addressed whether the existing mainstream music

market rejected on-demand music streaming, required quantitative analysis of archival

sales data. If the majority of existing music consumers rejected on-demand music

streaming, the introduction of on-demand music streaming would have had little to no

impact on consumption of existing formats. Graphical analysis of sales by format

including CDs, Digital Downloads, and On-Demand Streams for 2008 through 2010

illustrated changes in the relative positions of each format during the period. A Pearson r

 

 

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correlation then demonstrated any possible relationship between changes in consumer

activity relative to existing music formats and the introduction of on-demand music

streaming.

Addressing the fourth question, which asked whether existing low-end consumers

and/or non-music consumers embraced on-demand music streaming in its early stages,

required quantitative analysis of the sales data, this time organized by genre (Pop, Rock,

etc.). The researcher sorted data for each genre according to music format with each

format calculated as a percentage of total music consumption for the genre. The

researcher then identified genres with on-demand streaming as a higher percentage of

overall consumption than that of the overall market as early adopters. Finally, the

researcher examined early adopting genre for any patterns or trends that would indicate

whether on-demand streaming was coming from existing consumers, indicated by a low

willingness to buy, or new consumers, indicated by a low paid-to-non-paid activity ratio.

In addition to quantitative analysis, the researcher also examined qualitative data in the

form of historical media reports and journal articles for any evidence that might have

indicated why consumers were adopting on-demand streaming early.

Examining the fifth question, which asked whether on-demand music streaming

improved over time in the core dimension of portability, required similar analysis to that

needed for questions one and two. Historical documents dated from 2011 and later,

tracked consumer reactions to changes in on-demand streaming platforms. The researcher

then sorted and examined comments and opinions related to the competitive dimensions

previously examined in questions one and two.

 

 

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The sixth and final question, which inquired as to the encroachment of on-demand

music streaming upon the sales of existing formats, required quantitative analysis of

archival sales data from 2011 through 2017. Again, the researcher used graphical analysis

to illustrate weekly sales by format for the period of 2011 through 2017 to examine

changes between the relative positions of each format. A Pearson r correlation analysis

provided evidence of any possible relation between changes in on-demand music

streaming and changes in other formats.

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