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WAS ON-DEMAND MUSIC STREAMING A

DISRUPTIVE INNOVATION?

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

Leadership and Professional Practice

May 2019

WAS ON-DEMAND MUSIC STREAMING A

DISRUPTIVE INNOVATION?

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

Dean Diehl

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.

iii

ABSTRACT

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.

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

Summary of Findings ................................................................................................ 83

Limitations ................................................................................................................. 87

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

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

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,

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

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

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

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

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).

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).

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,

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,

“(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

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

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

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?

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

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

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

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

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

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.

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.

CHAPTER TWO

REVIEW OF THE LITERATURE

…innovation is the real driver of progress—Bill Gates

“The enterprise that does not innovate ages and declines. And in a period of rapid

change such as the present, the decline will be fast,” wrote management consultant,

educator, and distinguished author Peter Drucker (1985, p. 183). Managers of firms have

never been under more pressure to successfully introduce new products and ideas into the

marketplace as well as respond quickly to potentially disruptive moves from competitors.

As the common saying goes, “Innovate or die.”

Because of the pressure on business leaders to innovate, much research has gone

into how to innovate successfully. Since the mid-nineties, one of the key voices on the

topic of innovation has been Clayton Christensen, the Kim B. Clarke Professor of

Business Administration at Harvard Business School (Christensen, Raynor, & McDonald,

2015). Christensen’s book, The Innovator’s Dilemma (1997), and supporting articles are

mandatory reading for executive education programs across the United States including

MIT Sloan, Harvard Business School, and the Stanford Graduate School of Business

(Schmidt & Druehl, 2008).

In The Innovator’s Dilemma, Christensen (1997) introduced the theory of

disruptive innovation. A disruptive innovation is one in which the innovation is initially

inferior to existing options in the core competitive dimension most valued by opinion

leaders and mainstream consumers. However, the innovation survives in spite of this

inferiority because it is able to attract low-end consumers or brand new consumers by

excelling in a secondary competitive dimension or unique business model. Over time, the

disruptive innovation improves in the core competitive dimension while maintaining its

other advantages until it eventually becomes acceptable to mainstream consumers and, as

a result, encroaches on existing options, often displacing them entirely (Christensen et al.,

2015).

The popularity of Christensen’s work and the provocative nature of the term

disruptive innovation has had the adverse effect of diluting the actual theory as business

leaders and scholars adopted the term without truly understanding the related theory

(Christensen et al., 2015). This has led to an ongoing debate over what actually

constitutes a disruptive innovation as well as a consistent appeal throughout the literature

for more industries to apply disruption theory to specific innovations to clarify and

improve upon the theory (Christensen, 2006; Christensen et al., 2015; Danneels, 2004;

Schmidt & Druehl, 2008). The present study was in response to that appeal.

Determining whether the introduction and rise of on-demand music streaming in

the United States followed the pattern predicted by disruption theory required

examination of certain criteria. This study systematically compared each of those criteria

to archival sales data and historical documents. Specifically, this study tested the

following key questions:

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?

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?

Historical Perspective

Innovation, as a business concept, has its roots in the work of economist Joseph

Schumpeter (1928), who, in 1928, began to differentiate between invention and

innovation; concepts previously treated synonymously. In his article, The Instability of

Capitalism, Schumpeter (1928) defined innovation as “putting productive resources to

uses hitherto untried in practice” (p. 378, emphasis in original). Innovation, as

understood by Schumpeter, was a completely separate process from invention and served

purely economic purposes. According to Schumpeter, “It is quite immaterial whether this

[the process of innovation] is done by making use of a new invention or not”. He

continues, “…and even if it [an invention] be involved, this does not make any difference

to the nature of the process” (p.378). By 1942, Schumpeter, in his work Capitalism,

Socialism and Democracy (1942), went so far as to describe innovators and innovations

as forces of “creative destruction” (p. 82-83) disrupting stabilized markets and spurring

economic growth.

Schumpeter’s view of innovation changed the way academics, business leaders,

and even governments understood change and competition (Nicholas, 2003). For the first

time, innovation was “understood as a process” (Green, 2013, p. 3) as opposed to the

work of individual geniuses operating alone. Firms began to create research and

development departments, and innovation became a natural part of the business cycle

(Green, 2013).

Firms, focusing on innovation as a process of methodical research and

development, soon realized that “no matter what their advantages, newer technologies are

not adopted by all potential buyers immediately. Rather, a diffusion process is set into

motion” (Norton & Bass, 1987, p. 1069). The fact that innovations spread or diffused

through a population as opposed to achieving simultaneous adoption expanded the

conversation around innovation to include marketing and communications. Everett

Rogers, a specialist in communications and assistant professor of rural sociology at Ohio

State University, began to study how innovations spread through rural communities

leading to the groundbreaking work, Diffusion of Innovations (Dearing & Singhal, 2006).

While not the first to study how innovations spread through social networks, Rogers was

the first to develop a full theory on the subject, a theory still embraced by academics and

practitioners today (Dearing & Singhal, 2006).

Through multiple field studies, Rogers (2006) discovered that the diffusion of an

innovation, when plotted cumulatively over time, formed an S-curve. He also found that

diffusion, when plotted on a frequency basis, formed a bell curve. Using the properties of

a normally distributed bell curve, Rogers (2006) divided social networks into five

categories relative to the timing and motivation of their adoption of innovations within a

network: innovators, early adopters, early majority, late majority, and laggards (see

Figure 2.1).

Figure 2.1 Diffusion of innovations over time (s-curve) and by frequency (bell curve)

(Rogers, 2006).

Alkamade and Castaldi (2005) summarized the process, “The diffusion of a new

product, or innovation in a network, often follows a gradual pattern. In the first stage a

few consumers (the innovators or early adopters) adopt, then consumers in contact with

them adopt, then consumers in contact with those consumers adopt, and so forth until the

innovation possibly spreads throughout the network” (p.4). In multiple studies, Rogers

(2003) found the key to the diffusion of an innovation was the area in the bell curve

between 10-20% of the population, where the new idea spread from early adopters to

early majority consumers. He realized that early adopters often serve as opinion leaders

within a social network determining what ideas would reach the majority market. “[O]nce

opinion leaders adopt and begin telling others about an innovation, the number of

adopters per unit of time takes off in an exponential curve” (Rogers, 2003, p. 300).

In Diffusion of Innovations, Rogers (2003) identified five attributes of innovations

that influenced whether or not an innovation would successfully diffuse through a

population: 1) relative advantage, or “the degree to which an innovation is perceived as

being better than the idea it supersedes” (p. 229); 2) compatibility, or “the degree to

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

and needs of potential adopters” (p. 240); 3) complexity, or “the degree to which an

innovation is perceived as relatively difficult to understand and use” (p. 257); 4)

trialability, or “the degree to which an innovation may be experimented with on a limited

basis” (p. 258); and, 5) observability, or “the degree to which the results of an innovation

are visible to others” (p. 258). The common thread that ran through all of these attributes

was the idea that innovations must somehow tie to the familiar. “Old ideas are the mental

tools that individuals utilize to assess new ideas and give them meaning” (Rogers, 2003,

p. 243).

Ideas that are incompatible with existing practices are often rejected (Rogers,

2003). As researchers and practitioners alike began to perceive the importance of opinion

leaders in getting their innovations to diffuse among their most profitable customers, they

began requiring research and development (R&D) departments to, as Leonard (2006)

wrote, “tie their inventions to the bottom line of the company.” Leonard continued,

“Relevance became the mantra for research” (p. 88). In many industries, this resulted in

customer-driven innovation where established firms became captive to the needs of their

largest, most profitable customers; a condition that left established firms blind to all else

and open to attack from new market entrants willing to innovate in other ways

(Christensen, 1997).

Disruptive Innovations

In 1997, Clayton Christensen published a series of articles as well as the book,

The Innovator’s Dilemma, which introduced the idea of disruptive innovation theory. The

innovator’s dilemma, according to Christensen (1997), is that established firms in an

industry often restrict innovation only to those products or services that best serve

existing high-end customers, called mainstream customers. However, “in trying to please

high-end customers with regard to a key performance dimension, an incumbent

eventually develops a product that ‘overshoots’ the performance needs of mid to low-end

customers along that key dimension” (Schmidt & Druehl, 2008, p. 352). This over-

performance leads mid to low-end customers to look to other performance dimensions for

improvement, which opens the door for disruption (Adner, 2002).

Christensen (1997) offered that the reason leading firms often fall to new market

entrants is not due to bad management, lack of technical expertise, or shortsighted vision.

He stated, rather, their failure is due to their ties to an existing value network, or, “the

context within which a firm identifies and responds to customer’s needs, solves problems,

procures input, reacts to competitors, and strives for profit” (p. 32). In other words, they

become captive to their existing customers (Christensen, 1997). The dilemma for

established firms, then, is that evaluating each new innovation against the needs of

mainstream customers is exactly what established firms should do, but, in doing so, they

open themselves up to disruption (Christensen, 1997).

Focused on the needs of their mainstream customers, established firms reject or

ignore innovations that are inferior to existing options in the core competitive dimensions

preferred by opinion leaders within the existing market. Sometimes these innovations

survive, though, because they find a place among low-end consumers or an entirely new

market due to a unique business model or to their performance in some secondary

competitive dimension overlooked by established firms. Disruption occurs when these

innovations improve to the point that, in addition to the low-end or new market

customers, the high-end customers start to adopt them as well (Schmidt & Druehl, 2008).

The simple fact that an innovation disrupts or encroaches upon existing products

does not make the innovation a disruptive innovation, as defined by the theory (Schmidt

& Druehl, 2008). It is the path the innovation takes to disruption that matters (Christensen

et al., 2015). Most innovations replace the previous generation of products because they

improve upon those things most valued by existing customers. For example, DVD

players completely displaced VCR players because existing customers preferred the

higher resolution, smaller size, and greater durability of DVDs to VHS tapes (Caron,

2004). However, the DVD player, while disruptive, was not a disruptive innovation. It

was what Christensen terms a “sustaining innovation” (Christensen, 1997, p. 10).

The key difference between a disruptive innovation and a sustaining innovation is

in the adoption process. With a sustaining innovation, the mainstream customers of the

existing market adopt the new product or service from the start because it improves upon

the core competitive dimension they prefer. However, with a disruptive innovation, the

early adopters come from the fringes of the existing market or an entirely new market.

Christensen’s (1997) main illustration of a disruptive innovation used throughout

The Innovator’s Dilemma comes from the computer disk drive industry. In the late 1970s

and early 1980s, the core performance dimension most desired in disk drives by the

mainstream market was capacity (Christensen, 1997). And so, during that period, most

innovation driven by established firms focused on improving capacity, sometimes

through incremental improvement, at other times through “radically new technology” (p.

11). Christensen (1997) points out that every major technological innovation in disk drive

technology that progressed along the core dimension of capacity came from established

firms.

The story changed, though, when innovation turned a different direction. Entrant

firms new to the disk drive market began innovating along a different trajectory, the size

of the disk drive. These smaller drives had less capacity than existing models and the

mainstream market had no use for smaller drives with less capacity than their current

options. However, the smaller drives soon found a place in the nascent desktop personal

computer market (Christensen, 1997). Over time, these smaller drives increased in

capacity until the mainstream computer market began adopting them as well

(Christensen, 1997).

The disk drive illustration highlights several key attributes of disruptive

innovations. First, part of what creates the opportunity for a disruptive innovation is

performance oversupply. Adner (2001) states, “Christensen introduces the idea of

‘performance oversupply’ to explain the mainstream consumers’ decision to adopt

disruptive technology in the face of superior incumbent technology. The principle of

performance oversupply states that once consumers’ requirements for a specific

functional attribute are met, evaluation shifts to place greater emphasis on attributes that

were initially secondary or tertiary” (Adner, 2001, p. 669). In the case of disk drives, the

capacity of drives eventually exceeded what the existing market needed, which led

consumers to shift their focus from capacity to size (Schmidt & Druehl, 2008).

Second, entrant firms are usually responsible for introducing disruptive

innovations. According to Hunt (2013), “To an increasing degree, there has come to be a

tendency to bifurcate innovation into two contrasting sources: revolutionary

breakthroughs emanating from entrepreneurial firms and incremental enhancements

emanating from large, established incumbents” (p. 151). Because established firms focus

on the immediate needs of their high-end customers, they leave room for smaller new

entrant firms to target the fringes of the market or develop new markets (Christensen,

1997). Hunt (2013) continues, “The essence of this argument rests upon the belief that

innovation stemming from existing sources of knowledge will favor large incumbents.

Meanwhile, nascent-stage firms are expected to excel under circumstances that neither

require nor benefit from established organizational routines” (p. 151).

Third, most disruptive innovations only succeed in moving into the mainstream

market once they have improved in the core dimension sufficient to meet the needs of

mainstream consumers (Christensen et al., 2015). “[W]ith continual upgrading, the new

product eventually becomes acceptable even to the high-end customers of the old

product” (Schmidt & Druehl, 2008). While it is true that established firms, through

performance oversupply, exceed the mainstream market’s needs in the core performance

dimension causing the market to shift its focus, there is still a minimum performance

threshold in the core dimension that must be met by a new product or service before the

mainstream can accept it (Christensen, 1997).

Fourth, disruption is usually not the result of a single entrant firm. In a 2015

article for the Harvard Business Review, Christensen et al. (2015) wrote, “What we’ve

realized is that, very often, low-end and new-market footholds are populated not by a

lone would-be disrupter, but by several comparable entrant firms whose products are

simpler, more convenient, or less costly than those sold by incumbents” (p. 10). This was

certainly true in Christensen’s disk drive illustration where multiple entrant firms were

competing in the smaller disk drive space.

Finally, in the same Harvard Business Review article, Christensen et al. (2015)

stated, “Disrupters often build business models that are very different from those of

incumbents” (p. 7). Innovative products and services disrupt the market share of

established firms, but innovative business models erode the established firms’

profitability. For example, the ability to order and stream movies in your home allowed

Netflix to take away market share from video rental companies like Blockbuster.

However, Netflix’s monthly subscription model with no due dates or late fees destroyed

Blockbuster’s entire business model eventually leading to its bankruptcy (Christensen et

al., 2015).

While these attributes of disruptive innovations are often present, they are not all

present in every case. This begs the question, what are the essential elements of a

disruptive innovation that must be present in order for the theory to apply? In other

words, what, exactly, is disruption innovation theory and why does it matter?

Disruptive Innovation Theory: An Outline

According to Danneels (2004), “A disruptive technology is a technology that

changes the bases of competition by changing the performance metrics along which firms

compete” (p.249). To elaborate, according to disruption theory, an innovation that fits the

theory not only disrupts existing products and firms, but it does so in a very specific

manner. It changes the dimension along which innovation occurs, for example, shifting

innovation in disk drives from a focus on building capacity to a focus on size

(Christensen, 1997).

For an innovation to be a true disruptive innovation, it must initially be inferior in

the core competitive dimension preferred by the industry’s existing mainstream

consumers (Schmidt & Druehl, 2008). At a time when the core competitive dimensions in

video rental were convenience and selection, Netflix only offered a handful of titles and

required you to wait several days to get a DVD in the mail (Satell, 2014). Netflix was the

opposite of what mainstream video rental customers wanted making Netflix a classic

example of how disruptive innovations seem inferior to existing mainstream customers

when they first enter the market (Christensen et al., 2015).

Because it is inferior in the core competitive dimension, existing opinion leaders

and mainstream customers reject the disruptive innovation. As a result, established firms

ignore the innovation seeing it as no threat to their business (Schmidt & Druehl, 2008).

Video rental chains and movie studios alike ignored Netflix due to its inferiority to

existing options in convenience and selection. Jeff Bewkes, then CEO of entertainment

giant Time Warner, was quoted by the New York Times as saying, in reference to Netflix

posing a threat to existing film distribution models, “It’s a little bit like, is the Albanian

army going to take over the world? I don’t think so” (Arango, 2010, para 3). Reed

Hastings, president and founder of Netflix, in an interview for New Yorker Magazine,

said he wore Albanian Army dog tags for a whole year after the New York Times article

came out (Auletta, 2014).

In spite of rejection by mainstream consumers, a disruptive innovation survives

because it appeals to consumers on the fringes of the existing market or creates a new

market by appealing to them through some overlooked secondary dimension or unique

business model (Schmidt & Druehl, 2008). Netflix found an early base among classic

movie enthusiasts, early adopters of DVDs, and online shoppers who liked choosing their

movies from home. In addition, Netflix’s subscription model, which avoided due dates

and late fees, contributed to its early success as well (Satell, 2014). These marginal

consumers were less valuable to established firms like Blockbuster and so they continued

to ignore Netflix (Auletta, 2014).

Over time, a disruptive innovation improves along the existing market’s core

competitive dimension while maintaining its unique attributes with which it attracted its

initial customers (Schmidt & Druehl, 2008). As broadband internet expanded across the

country, Netflix was able to move into on-demand video streaming giving customers a

broader selection and greater convenience than the local video rental store. Now, with

Netflix, consumers could choose and watch new-release movies immediately without

leaving their home (Christensen et al., 2015). At the same time, Netflix, no longer tied

exclusively to physical DVDs, was able to expand its offering of classic movies and

television programs to satisfy their existing customers as well (Satell, 2014). Finally, the

established video market responded, and Blockbuster, seeing the increased threat from

Netflix, tried to enter the online market in 2004 spending over $500 million in an attempt

to dislodge Netflix (Abkowitz, 2009).

Due to the increased performance in both dimensions, the existing market’s high-

end consumers, who had originally rejected the innovation, begin to adopt. The disruptive

innovation, then, encroaches upon the market share of established firms (Schmidt &

Druehl, 2008). Upon introduction of on-demand streaming, mainstream video rental

customers began using Netflix causing a major shift in market share away from

Blockbuster and other video rental chains (Christensen et al., 2015). In addition, Netflix’s

subscription model changed the way consumers thought about paying for video content.

As traditional video rental stores, including Blockbuster, attempted to emulate the

subscription model by modifying existing rental and late fee structures, their overall

business model became untenable leading Blockbuster to declare bankruptcy in 2010

(Satell, 2014).

Netflix was a disruptive innovation, not simply because it led to the bankruptcy of

Blockbuster, but through the means by which it disrupted the video rental market. Netflix

found a fringe market that valued its online interface and its subscription business model.

It then innovated its way into the core of the market through improvement in the core

competitive dimensions of convenience and selection. Most importantly, it shifted the

existing market to a new subscription-based business model, which the established

market tried, but could not imitate without destroying their existing value network

(Christensen, et al., 2015).

Redbox, the kiosk-oriented video rental business, however, was not a disruptive

innovation, but a sustaining innovation. Founded in 2002, Redbox was initially a

subsidiary of McDonalds Corporation. Unlike Netflix, Redbox had immediate appeal to

mainstream video renters in the core competitive dimension of convenience. Redbox

offered consumers a relatively small selection of the most popular movie titles through

kiosks located conveniently at local McDonalds, grocery stores, and drug stores. Using

Redbox was fast, simple and, at $1 per night, far cheaper than existing options (Tryon,

2011).

Redbox, while disruptive to traditional video rental stores, was a sustaining

innovation because it immediately improved upon a competitive dimension important to

mainstream video rental customers (Christensen et al., 2015). It was a cheaper, easier,

faster way for mainstream video rental customers to get what they wanted (Tryon, 2011).

Netflix, by contrast, was less convenient at first and, therefore, posed less of a threat to

established firms when first introduced (Christensen et al., 2015).

In addition to disruptive and sustaining innovations, you also have failed

innovations. Keeping with movies, in 2007, Walgreens announced they would be

installing DVD-burning kiosks in their stores. These kiosks would allow consumers to

select older, out-of-print movies and burn them directly to a blank DVD while they

waited (Zeidler, 2007). DVD-burning kiosks were inferior to existing options, as was

Netflix, but, unlike Netflix, they never found a fringe market nor did they create a new

market. In 2008, Redbox announced they had negotiated a major distribution deal with

Walgreens who dropped the kiosk idea before full implementation in favor of the more

proven Redbox model (Reuters, 2008).

Disruptive innovation theory does more than simply identify what constitutes a

disruptive innovation. Christensen’s (1997) purpose in The Innovator’s Dilemma was to

help established firms better identify and respond to disruption. The three-fold illustration

of Netflix, Redbox, and DVD-burning kiosks highlights why Christensen’s (1997) theory

of disruptive innovations matters. Christensen (2015) wrote, “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). Each of the above examples required a different response from

market leaders.

According to the theory of disruptive innovations, sustaining innovations require

immediate response from established firms. Sustaining innovations improve upon the

core competitive dimension preferred by high-end consumers and so immediately

threaten the market share and profitability of established firms (Christensen et al., 2015).

Looking at innovation in video rental services through the lens of Blockbuster, Redbox

was the innovation that required the fastest response. While Redbox kiosks had relatively

few titles, the $1 price and convenient locations made them an immediate threat (Tryon,

2011). However, Blockbuster ignored them. As late as 2008, the newly appointed

Blockbuster CEO, Jim Keyes, stated in an interview, “Neither Redbox nor Netflix are

even on the radar screen in terms of competition” (Munarriz, 2008, paragraph 13).

While the ignoring of Netflix by Blockbuster at least made sense, Redbox was a

direct and immediate threat to their core business and should have elicited a response.

Blockbuster was right, though, in ignoring DVD-burning kiosks. The kiosks, with their

slow burn time and old titles, did nothing to encroach upon Blockbuster’s core business

of front-list movies and, therefore, were inferior in the core competitive dimension.

Knowing how to respond to various types of innovation is a critical part of strategy and

requires leaders to be able to recognize what sort of threat they are facing (Christensen et

al., 2015).

Innovation in Music Playback Media: A Brief History

Turning from movies to music, in order to examine on-demand streaming of

music in the United States to determine if it performed in accordance with disruptive

innovation theory, it is necessary to recount the evolution of music playback media. If on-

demand music streaming constituted a disruptive innovation, the only way to demonstrate

that fact would be to show how it performed within the context of the history of music

playback media. That being the case, a brief account of that history is included here.

The modern era of sound recording began in 1948 with the introduction of the 12-

inch, 33 1/3 rpm Long-Play Record (LP) by Columbia Records. Prior to the LP, there

were multiple playback media vying to become the standard for all recordings (Shayo &

Guthrie, 2005). Not only did the LP become the standard for its day, it is still the choice

today among a small number of music enthusiasts who prefer the sound quality of the LP

to modern playback options (Coleman, 2003).

For almost twenty years the LP reigned unchallenged until 1964 when Philips

introduced the Compact Cassette (cassette) soon followed in 1965 by the 8-Track Stereo

8 (8-track), created by William Lear, the inventor of the Lear Jet (Coleman, 2003). The

cassette and 8-track formats battled it out for over a decade with 8-tracks initially

winning due to support from the auto industry. Car manufacturers, looking for an audio

system that would allow drivers to bring their own music with them to the car, heavily

backed the 8-track format (Coleman, 2003). Eventually, though, the cassette’s advantages

in sound quality, playback length, smaller size, and recordability combined with the

introduction of the highly portable Sony Walkman cassette player, allowed the cassette to

win out over the 8-track even among car manufacturers (Coleman, 2003).

In 1982, through a partnership between Sony and Phillips, the Compact Disc (CD)

entered the market, bringing music playback media into the digital era (Shayo & Guthrie,

2005). The CD was the full package with high-fidelity sound rivalling the LP and greater

portability and durability than the cassette. While playback systems were initially

expensive, as prices dropped and car manufacturers began replacing cassette decks with

CD players, CDs eventually displaced cassettes entirely and relegated LPs to novelty

status (Coleman, 2003).

The next chapter in music playback history took place in Germany where Karl-

Heinz Brandenburg, working for the Fraunhofer Institute, invented the technology that

would eventually make MPEG Audio Layer III (MP3) files possible (Bellis, 2017).

Utilizing digital compression technology, the MP3 allowed the direct exchange of digital

music files without the aid of any physical playback media. Although Brandenburg

introduced the technology in 1991, it took several years to standardize and commercialize

the process. By 1996, standards were set, and the Fraunhofer Institute received a United

States patent for the MP3 (Bellis, 2017). However, in 1997, the core software for MP3

technology was stolen by an Australian college student unleashing a wave of illegal

activity (Albright, 2015).

Following the rise of several unlicensed peer-to-peer MP3 trading sites such as

Napster and MP3.com, United States courts sided with record labels and publishers in

declaring such trading of MP3 files as violations of copyright law, however, for every site

taken down, several more took their place (Shayo & Guthrie, 2005). At least partial relief

from this flood of illegal file trading came in 2003, when Apple, Inc. (Apple) opened its

iTunes Music Store; the first commercially viable site for the direct legal sale of digital

music files to consumers, called digital downloads (Coleman, 2003).

Legal digital downloads proved immensely popular, particularly as the iPod

music player became more affordable (Albright, 2015). Even early MP3 players allowed

users to store up to 60 hours of digital music in a player the size of a deck of cards

(Coleman, 2003). Between 2003 and 2013, Apple’s iTunes website dominated the sales

of digital downloads with market share exceeding 60% at times (Bostic, 2013).

Simultaneous with the invention of the MP3, other online music models were also

in development including music streaming. In general, music streaming involved the

playing of music directly from a website as opposed to downloading a file to a hard drive.

From the outset, music streaming developed along two different paths: programmed

streaming and on-demand streaming (Passman, 2015).

Programmed streaming, introduced by sites such as Last.FM (2003) and Pandora

(2005), allowed listeners to choose the style of the music they wanted, but not individual

songs (Passman, 2015). Considered legally as broadcasts, similar to radio, these sites did

not require direct licenses with content owners but paid for songs and recordings through

Performing Rights Organizations (PROs) such as ASCAP, BMI, SESAC and Sound

Exchange. The fact that programmed streaming sites did not require direct licenses with

content providers meant they could offer hundreds of thousands of songs without having

to negotiate with content owners (Passman, 2015). Considered a broadcast as opposed to

paid consumption, programmed streaming falls outside the scope of the present study

except as a free alternative to paid platforms much in the same manner as radio airplay.

On-demand streaming, unlike programmed streaming, allowed consumers to

choose the exact artist and song they wished to hear. On-demand sites were required to

secure direct licenses with content owners (Keene, 2016). The need for direct licenses

with content owners created a significant barrier to entry for on-demand streaming sites

and limited the number of titles such sites could initially offer. Because on-demand

streaming was a direct substitute for other music playback media, Nielsen tracked and

reported on-demand streaming as a part of total music consumption in the US (Nielsen,

2018).

The first legal on-demand streaming site to appear in the US was Rhapsody,

introduced by Listen.com in 2001 (Passman, 2015). As opposed to the purchase model

offered by iTunes, where consumers paid for and owned individual song and album files,

Rhapsody users paid a monthly subscription fee to have instant access to all of the songs

in the Rhapsody catalog. While Rhapsody initially only offered subscribers access to

several thousand songs, many of which were from smaller, independent artists and labels,

by 2002, Rhapsody had secured direct licenses with all of the major labels and offered a

catalog of 175,000 songs available for immediate access to subscribers (Evangelista,

2002).

While content owners, including most of the major labels and publishers,

provided licenses to Rhapsody and other on-demand streaming sites, they did so with

several restrictions. Concerned about providing a too-convenient alternative to music

purchases, content owners only allowed sites such as Rhapsody to offer on-demand

streaming through a computer connected to the internet. In addition, because on-demand

streaming sites played music directly from a website as opposed to a file stored on the

listener’s device, the speed at which data transferred from the website to the listener’s

computer had a big impact on the music streaming experience. In 2001, at a time when

most online users were restricted to dial-up access to the internet, on-demand streaming

could often prove very frustrating.

On-demand streaming, in its initial format as offered by platforms like Rhapsody,

could not compete with the dramatic launch of Apple’s iTunes platform. The iTunes

digital download model, with its emphasis on music ownership, ease of use, and ability to

play on multiple devices both online and offline, proved more popular than the more

innovative but restrictive on-demand streaming approach. While there was a fringe

market of consumers interested in the subscription model, the on-demand streaming

platform remained a novelty leading Apple founder Steve Jobs to famously state, “People

have told us over and over and over again, they don’t want to rent their music” (Ricker,

2015, para. 3).

By 2008, seven years after the launch of Rhapsody and the first year in which

Nielsen began tracking and reporting on-demand streaming as a form of music

consumption, on-demand streaming accounted for less than 1% of total music

consumption in the US (Nielsen, 2018). In that same year, digital downloads accounted

for 31% of total music consumption. In the first round between digital downloads versus

on-demand streaming, the digital download was the clear winner.

In 2011, a new wave of on-demand streaming sites entered the US, led by Spotify,

a Sweden-based company that had been making headlines in Europe since 2008. Through

a lengthy negotiation process with US music content owners, Spotify was able to offer

something no other on-demand streaming platform had before, a free on-demand

streaming subscription. Utilizing a business model built on the sharing of advertising

revenue with content owners, called ad-supported streaming, Spotify provided users with

free access to a catalog of 15,000,000 songs (Sisario, 2011).

While content owners insisted upon several restrictions on Spotify’s free ad-

supported subscription users, including limiting the service to computer use only and a

monthly 10-hour cap on free listening, the service grew rapidly surpassing 20,000,000

users by 2013 (Spotify, 2013). Not only did free on-demand subscriptions grow rapidly,

Spotify’s paid subscriptions grew as well, mostly through free users converting to a paid

subscription in order to take advantage of additional features such as ad-free, unlimited

access on computers and mobile devices both online and offline (Ingham, 2016).

Spotify’s entry into the market was soon followed by on-demand streaming

platforms from Tidal, Amazon, Apple, Pandora and YouTube. By the end of 2017, on-

demand music streaming accounted for 65% of all music consumption in the US. In that

same period, digital downloads fell to less than 20% of consumption. On-demand

streaming is now the most dominant music playback platform since the peak of the CD at

the beginning of the 2000s (Nielsen, 2018).

Digital Downloads: A Sustaining Innovation

While it falls outside the present study to conduct an in-depth inquiry into the

introduction and rise of digital downloads as a music playback format, an abbreviated

analysis of digital downloads provides an excellent background for the inquiry into on-

demand music streaming. As was stated earlier, the exchange of digital downloads began

in the late 1990s, mostly through illegal websites such as Napster. As this study is

concerned only with legal transactions, this discussion regarding digital downloads

begins with the launch of iTunes in 2003.

The key concept in disruptive innovation theory is that a disruptive innovation is

initially inferior in the core competitive dimension preferred by mainstream consumers.

When innovations are inferior in the core competitive dimension, mainstream consumers

reject them (Schmidt & Druehl, 2008). However, mainstream consumers embrace

innovations that outperform existing options in the core competitive dimension.

Christensen (1997) labels these latter forms of innovation as sustaining innovations.

As this study will later demonstrate, the core competitive dimension in music

playback formats was portability with each successive innovation, from the LP to the CD,

improving upon the portability of music (Albright, 2015). For digital downloads to have

been a disruptive innovation, they would have been less portable than the CD, the

standard playback format at the time. However, that was not the case as digital

downloads were more portable than any format at the time (Lazrus, 2016).

Digital downloads, as electronic files, were easier to store, easier to transport, and,

to the chagrin of content owners, easier to transfer to others (Plambeck, 2010). A simple

MP3 player could store the entire music collection of an average listener in a device the

size of a deck of cards (Flanagan, 2013). Digital downloads were, and many would say

still are, the most portable music playback format available.

That said, while digital downloads were superior in the core competitive

dimension, they never fully replaced the CD. In fact, by 2011, a full decade after the

appearance of iTunes, digital downloads, whether in the form of full albums or individual

songs, only accounted for 49% of the sales of music (Nielsen, 2018). To understand why

this might have been the case requires a return to diffusion theory.

In his classic work, Diffusion of Innovations, Rogers (2003), identified five

attributes of innovations that influence diffusion. While disruption theory largely emerges

from the attribute of compatibility, the remaining attributes are always at play as well.

One of the attributes at play with the introduction of digital downloads was that of

complexity.

Complexity, defined by Rogers (2003) as “the degree to which an innovation is

perceived as relatively difficult to understand and use” (p. 257), was an important

element in the adoption of digital downloads due to the shift from tangible to intangible

media. From the LP to the CD, consumers purchased a tangible item they could hold,

carry, lend, and store; something they could possess. With the digital download,

consumers received an intangible computer file without packaging, album art, or liner

notes. There were also limitations on the rights of ownership, particularly in the area of

sharing music with others (Lazrus, 2016). This shift from the tangible to the intangible

added complexity to the adoption process for the average music consumer.

In addition, while the free iTunes software made it easy to download, play, and

store digital downloads using a typical computer or laptop, fully taking advantage of the

enhanced portability features offered by digital downloads required the purchase of a

designated MP3 player. As with the adoption of any new media format, adoption of

digital downloads required adoption of the necessary hardware. Initial attempts at

creating portable MP3 players were clunky, expensive, and difficult to use. However,

with the introduction of the iPod in 2001, Apple once again smoothed the way for the

adoption of digital downloads (Flanagan, 2013).

Another critical factor to consider in the introduction of digital downloads was the

unbundling of the album. Prior to digital downloads, most music playback formats

consisted of bundles of ten to twelve songs referred to as albums. If consumers heard a

song they liked, they were often required to buy the full album in order to get the song.

While some single songs were available on CD, by the time digital downloads released,

there were very few on the market.

One of the key features of the iTunes store, when it first launched, was the

unbundling of albums, which allowed consumers to buy only the song they wanted as

opposed to being required to purchase the full album (Elberse, 2010). This ability for

consumers to purchase one song at a time somewhat masked the adoption of digital

downloads due to the way Nielsen accounted for music sales. Music sales charts created

by Nielsen relied on album equivalents with ten single-song digital downloads counting

as an album. While this method allowed comparison between CDs and digital downloads,

it distorted consumer activity in that one consumer buying a CD had the same impact on

the charts as ten consumers buying individual song digital downloads. What appeared as

a 1:1 relationship between CDs and digital downloads was actually a 1:10 relationship

when examined at the consumer behavior level. This distortion of consumer behavior

somewhat obscured the rise of downloads as a music playback format (Nielsen, 2018).

One final factor in looking at digital downloads is the underlying business model.

The business model offered through iTunes was a direct continuation of the retail

purchase model used in the sales of music media since recorded music first became

commercially available. This continuation of the purchase model was a critical factor in

calling digital downloads a sustaining innovation as opposed to a disruptive innovation.

The critical point to take away from this brief look at digital downloads is that

when an innovation enters an existing market; there are many factors at play (Rogers,

2003). Conditions apparently caused by a lack of continuity may actually be a

combination of complexity, trialability, or one of the other attributes of innovations that

affect adoption. This is partially why identifying disruptive innovations is so difficult

(Christensen et al., 2015).

Conclusion

In analyzing whether an innovation is a true disruptive innovation, the main thing

to keep in mind is that a disruptive innovation shifts the basis of competition (Danneels,

2004). Netflix shifted the video rental business to a monthly subscription model

(Christensen et al., 2015). Digital downloads, though, competed and survived by

excelling in the existing core competitive dimension of portability. That is why Netflix

was considered a disruptive innovation, and digital downloads were not.

In studying on-demand music streaming, it is important to isolate consumer

behavior using both quantitative and qualitative data to uncover not only what happened,

but also why it happened. Was on-demand music streaming inferior in regards to

portability? Did the mainstream consumers initially ignore on-demand music streaming?

Finally, has on-demand music streaming caused a shift in the competitive dimension

most favored by consumers? These are the questions this study hopes to address.

CHAPTER THREE

METHODOLOGY

It is critically important that managers be able to recognize a disruptive

innovation when they see one. –Glenn Schmidt

A review of the literature surrounding innovation in general and, more

specifically, disruptive innovation, reveals much confusion and misinformation regarding

what constitutes a true disruptive innovation. While all innovation is to some extent

disruptive, a disruptive innovation, as defined by the theory put forward by Clayton

Christensen in his book, The Innovator’s Dilemma (1997), follows a very specific

pattern. It is how an innovation disrupts, not just the simple fact that disruption occurred

that matters (Schmidt & Druehl, 2008).

A true disruptive innovation, according to Daneels (2004), “changes the bases of

competition by changing the performance metrics along which firms compete” (p. 249).

Netflix shifted the home video market from a purchase model to a subscription model

(Satell, 2014). Airbnb provided low-cost accommodations for low-end travelers through a

network of private homeowners and then advanced into high-end tourist rentals

(Guttentag, 2015). Online greeting cards, digital photography, smart phones, and

ultrasound technology were all disruptive innovations when introduced (Christensen,

1997). In short, disruptive innovations shift the competitive basis within an industry from

one competitive dimension to another or from one business model to another by

innovating along a different trajectory than that upon which previous innovation had

occurred (Schmidt & Druehl, 2008).

The present study is concerned with the question as to whether on-demand music

streaming performed in accordance with Christensen’s (1997) theory of disruptive

innovation. In order for on-demand music streaming to be a disruptive innovation, certain

criteria must apply. The following research questions summarize those criteria:

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?

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?

Research Design

This study utilized a mixed-methods research design, drawing from both

quantitative and qualitative archival documents and data sets. Examining the patterns of

innovation within an industry requires, as Christensen (1997) stated, “carefully

reconstructing the history of each technological change in the industry [so that] the

changes that catapulted entrants to success or that precipitated the failure of established

leaders could be identified” (p. 8). In order to reconstruct both what happened and why it

happened with the introduction and rise of on-demand music streaming, the researcher

required quantitative analysis of archival sales data as well as qualitative analysis of

historical documents in the form of corporate reports, official press releases, marketing

materials, news items, journal articles, interviews, and surveys.

Analysis of archival data is a common means of quantitative research. Fielding

(2004) pointed out that such analysis “informs many academic debates, much policy

analysis, and, though largely unpublished, the business decisions of many companies” (p.

98). In order to know what happened with an innovation, one needs to examine the

evidence left behind by archival records of transactional data.

The challenge in using this approach was gaining access to industry-wide

transactional data as opposed to data from only one company, or relying on second-hand

summary data from trade organizations or the media (Hand, 2018). This was because

private reporting agencies that archive industry-wide transactional sales data usually only

grant access to such data to qualified insiders within the related industry. In most cases,

only researchers willing to pay considerable fees can access transactional data of the kind

necessary for detailed analysis (Hand, 2018).

For the purpose of this study, The Nielsen Company (Nielsen), the primary

collector and distributor of entertainment marketing data, granted the researcher a special

license allowing access to their industry-wide sales data archives for music within the

United States (US). This data included weekly sales of every music playback format,

including physical albums (CDs, LPS), digital downloads, and on-demand music streams

from 2008 through 2017. This rare level of access to Nielsen’s archives for the purpose of

research allowed this study to examine on-demand music streaming using industry-wide,

weekly sales data of the kind necessary for the type of study recommended by disruptive

innovation scholars.

While the use of archival data was what Fielding (2004) referred to as “a well-

established practice in quantitative social research,” (p. 98), the use of archival

documents as a primary source for qualitative research was not as common. Historically,

in qualitative research, document analysis served only a supporting role to primary

research collected through interviews, focus groups, and observational data. However,

there has been a growing acceptance of archival material as a primary source due to, as

stated by Fischer and Parmentier (2010), “increasingly sophisticated critiques emerging

of interview data as a primary resource in qualitative research” (p. 799).

Interviews and surveys, long the preferred approach by most qualitative

researchers, have come into question by studies that show “disjunctions between what

people say and what people do” (Fischer & Parmentier, 2010, p. 799), or, in reference to

past events, what people remember and what actually occurred. When looking at past

events, such as consumer attitudes regarding an innovation introduced two decades

earlier, secondary analysis of archival documents may have, as Fielding (2004) described,

“a claim to greater plausibility since it is less likely that the analytic interests employed

will have played a part in the interactional field from which they were derived” (p. 100).

In other words, archival documents capturing consumer attitudes regarding on-demand

music streaming at the time of its introduction were not as subject to researcher bias.

Bowen (2009) defined document analysis as “a systematic procedure for

reviewing or evaluating documents—both printed and electronic (computer-based and

Internet transmitted) material” (p. 27). He went on to state that “documents may be the

most effective means of gathering data when events can no longer be observed or when

informants have forgotten the details” (p. 31). In attempting to reconstruct the history of

on-demand streaming, archival documents were a necessity. Corporate annual reports,

press releases, advertisements, magazine articles, archived corporate research, news

items, and journal articles all provided needed information towards answering the

research questions.

One final argument for the use of archival documents as a primary source for

research had to do with triangulation. Referring to the use of archival documents from

various sources, Fielding (2004) commented, “The activity may be useful in evaluating

the generalizability of findings from qualitative research by different researchers on

similar populations” (p. 98). By using archival documents, the researcher was able to

assemble multiple views generating a broader picture than would be possible by focusing

solely on one’s own qualitative research.

The overall design of this study followed, in many ways, the structure of a court

case, in which distinct criteria needed to be present in order for circumstances to fit the

theory of disruptive innovations. Each of the research questions represented one of those

criteria. As such, the researcher approached each question individually using different

combinations of archival data as needed. This method deviated from the typical research

project in which one master set of data is gathered and used to address a group of

questions. In other words, the overall question of whether on-demand music streaming

was a disruptive innovation was addressed one criteria at a time through the individual

research questions, each contributing to a final analysis in the end.

Participants

As stated earlier, Christensen (1997) described his method for examining

disruption within an industry as “reconstructing the history of each technological change

in the industry” (p. 8). This approach necessitated assembling data at the industry level as

opposed to a subset population within an industry. The data made available for the

present study included every commercial transaction of music within the US from 2008

to 2017. As global information was not available, this study was limited to examining

data for the US only.

The population for this study included all music consumers within the US from

2008 through 2017. MusicWatch (2018), a research company dedicated to music industry

market research and analysis, described the average overall music buying population

during the period as consisting of 221,000,000 people, 56% of which were female.

Whites made up 72% of the market, while Blacks represented 12%, and other ethnicities

constituted the remaining 16%.

MusicWatch (2018) only collects information on consumers older than 13. Based

on their research, 34% of music consumers tracked were between the ages of 13-24, 28%

were 25-34, 16% were 35-44, and the final 22% consisted of adults 45 and older. These

age estimates were reported with a +/- 1.75% margin of error. (See Table 3.1)

Table 3.1

U.S. Music Consumers

Age 13-24 34%

25-34 28%

35-44 16%

45+ 22%

Gender Male 44%

Female 56%

Ethnicity White 72%

Black 12%

Other 16%

Note . Reported accuracy of +/- 1.75

(MusicWatch, 2019)

Because reconstructing the history of technological change within the music

industry required examining not only what happened but also why it happened, the

researcher needed to find a way to examine consumer behavior at the transactional level.

To answer the research questions, it was necessary to know what kind of consumer first

adopted on-demand music streaming. Because Nielsen does not capture individual

consumer data at the time of transaction, another means of identifying consumer behavior

had to be established.

A further complication in analyzing consumer behavior related to music

consumption was the fact that the various music playback options were not mutually

exclusive. An individual consumer could purchase physical albums, buy downloads, and

use on-demand streaming all at once. This made isolating individual consumer behavior a

challenge.

Given these same challenges, previous researchers have turned to music genre as

a means of grouping and analyzing consumer behavior (Montoro-Pons & Cuadrado-

Garcia, 2016). Genre in music has been defined by Lena and Peterson (2008) as,

“systems of orientations, expectations, and conventions that bind together an industry,

performers, critics, and fans in making what they identify as a distinctive sort of music”

(p. 698). A genre, then, includes not only those who produce the music, but also the fans

who listen (Lena & Peterson, 2008). As such, genres, as Montoro-Pons and Cuadrado-

Garcia (2016) stated, “help in identifying specific preferences…that could be useful in

clustering individuals in specific social spaces, from which a deeper understanding of

consumer habits and behavior can be derived” (p. 3).

The data provided by Nielsen included not only industry-wide transaction data but

also breakout data by genre for the period examined. While some scholars account for as

many as 60 different music genres (Lena & Peterson, 2008), Nielsen tracks 16 core

genres. Of those 16 genres, this study examined nine core genres, including Pop,

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

World Music. This study excluded genres considered historical, non-commercial, or

novelty in nature, including Blues, Classical, Children, Comedy, Holiday/Seasonal, and

New Age. This study also excluded the Other category, a collection of uncategorized

artists and songs unlikely to exhibit the group behavior upon which the concept of genre

is based (Lena & Peterson, 2008). For future reference, the omission of these genres

meant that later genre-level analyses related to market share resulted in numbers that

equaled less than 100% of the total market.

To summarize, questions dealing with what happened in the introduction of on-

demand music streaming used comprehensive industry-wide data encompassing every

music consumer in the US from 2008-2017. Questions dealing with consumer attitudes

and behaviors regarding on-demand music streaming examined genre-specific

transactional data. The combination of the two approaches provided a broad picture of

consumer actions and attitudes.

Data Collection

Nielsen collects weekly transactional sales data for all music purchases within the

US. Retail outlets and online music providers such as Spotify, Apple, Amazon, and

Pandora report transaction data to Nielsen on a weekly basis through electronic

transmission. Nielsen converts the raw data and compiles various sales charts and

analytical tools available to their clients through their proprietary website and database.

Through a special license, the researcher accessed Nielsen’s archival database that

included weekly music transactions in the US from 2008 through 2017. The site featured

a report generation tool that provided access to industry-wide data or subsets of data

based on music genre. Table 3.2 demonstrates the format of the raw data before

preparation.

Table 3.2

Raw National Sales Data for the first two weeks of 2017

Week 1 2017 Week 2 2017

Total

Albums w/TEA w/SEA On-Demand 11,032,793 10,343,343

By Format

Physical Albums Sales 1,910,260 1,586,799

Digital Albums Sales 1,525,309 1,223,565

Digital Song Sales 14,261,486 12,649,377

Streaming On-Demand 9,256,613,749 9,402,061,633

(Nielsen, 2018)

Lucko and Mitchell (2010) referred to the process of data preparation as

“systematically collating and transforming unformatted, unconnected, or otherwise

initially unusable data into a consistent and coherent data set” (p. 49). The data initially

provided by Nielsen reported non-equivalent sales units that needed conversion into

equivalent units for comparison purposes. As the primary generator of sales charts and

marketing data, Nielsen established a standard for converting non-album purchases into

album equivalents. As would be expected, downloaded albums convert into albums at a

1:1 ratio. Downloaded individual songs convert into albums at a 10:1 ratio (Track

Equivalent Albums-TEA) with on-demand streams converting into albums at a 1,500:1

ratio (Stream Equivalent Albums-SEA) (Nielsen, 2018).

Converting all sales data into album equivalents allowed comparative analysis of

nonequivalent measures. The converted data, when added together, produced an album-

equivalent total permitting comparative analysis among the different music playback

formats. Table 3.3 shows what the data above looked like after conversion.

Table 3.3

Converted National Sales Data for the first two weeks of 2017

Week 1 2017 Week 2 2017

Total

Albums w/TEA w/SEA On-Demand 11,032,793 10,343,343

By Format

Physical Albums Sales 1,910,260 1,586,799

Digital Albums Sales 1,525,309 1,223,565

Digital Song Sales (converted) 1,426,149 1,264,938

Streaming On-Demand (converted) 6,171,076 6,268,041

(Nielsen, 2018)

Collection of qualitative data in the form of historical documents took place

through various library search engines as well as online search engines such as Google

Scholar. Historical documents generated for analysis included corporate reports, official

company press releases, archived marketing materials, journal articles, news reports, and

archived interviews with representatives of the industry. Where possible, peer reviewed

sources were used to triangulate data from non-academic sources such as new articles and

media interviews.

Analytical Methods

Quantitative analysis of archival sales records started with a graphical analysis of

sales by music format. Variables included sales of physical albums (CDs, LPs), full

digital albums, digital songs, and on-demand streams. The researcher graphed the data

using both the actual numbers as well as by percent of the weekly total (i.e., physical

albums as a percent of the album equivalent total for the week, etc.).

In addition to graphing the data, the researcher conducted a Pearson r correlation

analysis among the various playback formats to determine if there was any relationship

between shifts in activity between CDs, downloads, or on-demand streams. The

researcher used weekly transaction data sorted by playback format for the period

examined. The researcher experimented with more sophisticated time series analysis, but

none enhanced the results beyond what the Pearson r correlation revealed.

Qualitative analysis involved historical document analysis of both public and

private records, including corporate reports, official company press releases, archived

marketing materials, journal articles, news reports, and archived interviews with

representatives of the industry. The researcher collected documents specific to each

research question, compiling, classifying, and analyzing them in order to address the

problem stated within each question. The data collected allowed the researcher to, as

Bowen (2009) stated, “Understand the historical roots of specific issues and…indicate the

conditions that impinge upon the phenomena currently under investigation” (p. 30).

To answer questions one and two, the researcher needed to compare music

playback formats across several key competitive dimensions, including sound quality,

price, depth of offering, portability, as well as the relative business models involved. The

researcher examined documents such as official press releases, new reports, and platform

reviews in the media related to the various formats including LPs, cassettes, CDs, digital

downloads, and on-demand streaming. The researcher then compared the performance of

each format in the various competitive dimensions.

Question three, which addressed whether mainstream consumers rejected on-

demand streaming when it first appeared, required a mixed-method approach.

Quantitatively, the researcher pulled archived sales data sorted by playback format,

including CDs, digital download albums, digital download songs, and on-demand music

streaming, for the year 2008, the earliest period for which Nielsen tracked on-demand

music streaming. The researcher graphed and analyzed the transactional data for the

period. The researcher also conducted a Pearson r correlation analysis among the various

formats to determine the potential relationship between changes in on-demand streaming

and the other formats. The researcher also collected and analyzed qualitative archival

data related to early consumer opinions to triangulate conclusions drawn from the

quantitative analysis.

Question four was a two-part question, the first part of which examined whether

on-demand music streaming survived due to acceptance by the low-end of the existing

market or the establishment of a new market. To answer this part of the question required

the identification of low-end and non-consumers of music. In the literature surrounding

the theory of disruptive innovations, researchers seeking to identify low-end consumers

typically created a continuum based on a consumer’s willingness to pay for a product or

service (Schmidt & Druehl, 2008). High-end or mainstream consumers were those with

the highest willingness to pay for a product or service, while low-end consumers

exhibited less of a willingness to pay. Non-consumers, by extension, were those

unwilling to pay for a product or service (Schmidt & Druehl, 2008).

There were several challenges in examining willingness to pay when applied to

music consumers. First, there was a lack of data at the individual consumer level. In

addition, in music, willingness to pay could shift among individuals based on the specific

artist or song involved. Finally, music formats were not mutually exclusive, so the same

music consumer could purchase physical albums or downloads, stream songs, or listen to

the radio all at once (Montoro-Pons & Cuadrado-Garcia, 2016). Because of these

challenges, academic studies involving the music industry used genres as a means of

grouping and analyzing consumer behavior (Montoro-Pons & Cuadrado-Garcia, 2016).

Using sales data provided by Nielsen sorted by genre, the researcher calculated

the average percent of total consumption by music format including CDs, downloaded

albums, downloaded songs, and on-demand music streaming for the years 2008-2010.

Averaging data across multiple years minimized the potential skewing of data by major

blockbuster releases in any one year. By calculating the ratio of full album purchases

relative to total consumption, the researcher identified those genres with a full-album

purchase ratio lower than that of the total market. Because full-album purchases cost

considerably more than individual song purchases or streaming, the researcher considered

genres with a low album-purchase ratio to be the low-end of the existing market

(Montoro-Pons & Cuadrado-Garcia, 2016).

Establishing non-consumers of music required an examination of free media in

relation to paid activity by genre. The two main legal free choices for music listeners

were radio and programmed streaming, both of which Nielsen tracked and reported for

the period. The researcher used quantitative analysis of radio audience by genre as well

as programmed streaming transactions, again averaged across 2008-2010, to create a

paid-to non-paid activity ratio. Because radio audience numbers (r) and programmed

streaming numbers (ps) were several multiples higher than consumption numbers (c), the

formula c ÷ ((r ÷ 100) + (ps ÷ 100)) was used to develop a paid-to-non-paid activity ratio.

Genres with a lower paid-to-non-paid activity ratio than the total market represented

behavior reflective of non-consumers of music.

The final step in answering the first part of question four required identifying

genres that adopted on-demand music streaming at a rate faster than the overall market.

To do this, the researcher calculated streaming as a percentage of overall consumption by

genre. Once again, the researcher averaged the data across the years 2008-2010 to

minimize the impact of any one major blockbuster release. The researcher then compared

those genres with an adoption rate higher than the overall market with those genres

earlier identified as low-end or non-music consumers to determine if early adopters were,

in fact, low-end or non-music consumers.

The second part of question four asked whether early adopters switched to on-

demand streaming due to superiority in some secondary competitive dimension or

through the introduction of a new business model. The researcher used qualitative

analysis of historical documents to identify why early adopters chose on-demand music

streaming. This included an analysis of on-demand music streaming in each of the

competitive dimensions, including portability, sound quality, price, and depth of

selection. In addition, the researcher examined the underlying business model of on-

demand music streaming relative to existing formats.

Question five, which examined ongoing performance of on-demand streaming

platforms in relation to the key competitive dimensions of sound quality, price, depth of

offering, and portability, required qualitative analysis similar to that used for the first two

questions. Archived records, marketing materials, news stories, and consumer reports

provided insight into changes in the platforms over time as well as consumer reactions to

those changes.

Also used in addressing question five was a survey by MusicWatch (2018), a

music industry market research and analysis company that examined consumer

preferences among various features of on-demand music streaming services. Conducted

in February of 2018, MusicWatch surveyed a random selection of 2,495 on-demand

streaming service users, examining consumer preferences among various features of the

top on-demand music streaming services such as Spotify, Apple Music, Amazon

Unlimited, and Google Play Music among others (MusicWatch, 2018). In addressing

question five, the researcher identified specific issues related to the core competitive

dimensions from the MusicWatch report for analysis.

Finally, question six, which examined potential encroachment on existing formats

by on-demand streaming, required quantitative analysis of archived sales records. The

researcher graphed and analyzed the relative performance of each format for the period

between 2011 and 2017. The researcher also conducted a Pearson r correlation analysis

among the various formats for the period, including CDs, downloaded albums,

downloaded songs, and on-demand music streaming to examine and analyze changes

between the formats.

CHAPTER FOUR

FINDINGS AND CONCLUSIONS

Learning and innovation go hand in hand. The arrogance of success is to think that what

you did yesterday will be sufficient for tomorrow.—William Pollard

As business theories go, the theory of disruptive innovations is relatively new,

having emerged in the mid-1990s through the work of Clayton Christensen (Christensen,

1997). Christensen (2006), in his article “The ongoing process of building a theory of

disruption,” suggested that the testing of a theory improves the theory. Christensen stated,

“The deductive portion of a complete theory-building cycle can be completed by using

the model to predict ex post what will be seen in other sets of historical data” (p. 45). The

purpose of this study, therefore, has been to contribute to the field of disruption theory

through the testing of the model against the historical data related to the introduction and

subsequent rise of on-demand music streaming.

In short, disruptive innovations are inferior to existing options in the core

competitive dimension upon which previous innovation has occurred. The innovation

survives, however, because the low-end of the existing market or an entirely new market

prefers its superiority in some secondary competitive dimension or its unique business

model. Over time, the innovation improves in the core competitive dimension while

maintaining its other advantages to the degree that the mainstream of the existing market

begins to shift to the innovation thus disrupting existing options.

In order to test the model of disruptive innovation against the introduction and rise of

on-demand music streaming, the researcher addressed a series of questions, namely:

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?

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?

Findings

Using both quantitative and qualitative research methods, the researcher examined

archival records as well as historical sales data for the period between 2001 and 2017 in

order to reconstruct what happened through the introduction of on-demand music

streaming in the United States and how music consumers adopted on-demand music

streaming. The following presents the findings from this research.

Question 1: Was there a core competitive dimension along which innovation

occurred in music playback formats prior to the introduction of on-demand

streaming?

Sound quality, price, depth of offering, and portability have all been key

competitive dimensions in music playback media. In an examination of archival

documents relating to various media, from the LP to the digital download, only

portability improved with each successive innovation Table 4.1 illustrates how each

playback media compared along the various performance dimensions.

Table 4.1

Playback Media Performance

LPs Cassettes CDs Downloads

Sound Quality Strong Weak Strong Moderate

Depth of Offering Weak Weak Weak Strong

Price High Medium High Low

Portability Weak Strong Strong Strong

Sound quality would have seemed an obvious choice as a preference among

opinion leaders and high-end consumers, but most experts agree that cassettes and digital

downloads were actually inferior to either the LP or CD (Plambeck, 2010). Moreover,

while digital downloads were available in a high quality format that many experts say

rivalled the CD or LP, most consumers opted for smaller MP3 files or the cheaper version

of the Advanced Audio Coding (AAC) files used by iTunes, which have been sonically

proven to be inferior to both the CD and LP (Plambeck, 2010).

Another competitive dimension that often drives innovation is price. Retailers

priced cassettes similarly to LPs, but CDs were more expensive than cassettes and LPs

(Hochman, 1990). Digital downloads, though, when first introduced, were significantly

cheaper than CDs. When Apple launched the iTunes store in 2003 with single-song

downloads at 99 cents and full albums for $9.99, the average new release on CD cost $18

(Griggs & Leopold, 2013). Price as a core competitive dimension would have required

each innovation to drive prices downwards, and that was not the case.

Depth of offering refers to the amount of content available in each new playback

media upon introduction. With each successive innovation, the number of titles available

was relatively small with only 49 titles debuting on cassette when it first emerged

(Billboard, 1966) and 50 titles on CD for its initial launch (LEM, 2014). However, as

each new format gained popularity and playback hardware supporting the format diffused

through the population, labels released more and more content. Digital downloads were

the exception. According to an official press release from Apple, there were over 200,000

songs available when it launched the iTunes store in 2003 (Apple, 2003). While this

created an advantage for digital downloads, for depth of offering to be the core

competitive dimension, each successive innovation (cassette, CD, and digital download)

would have shown improvement along this dimension and, again, that was not the case.

The final competitive dimension considered was portability, or the ability for the

consumer to take their music with them. While there were attempts to make the LP

portable, the resulting technology was impractical and only adopted by a fringe market of

consumers (Gopinath & Stanyek, 2014). Cassettes (and 8-tracks), however, were

specifically created to provide the portability LPs lacked (Coleman, 2003). CDs provided

even more portability due to their thinness and greater durability over the cassette. CDs

also provided a significant increase in sound quality, which allowed them to become the

dominant music playback format by the late 1980s (Coleman, 2003). Digital downloads

provided a vast improvement in portability over all previous formats. With digital

downloads, you could carry your entire music catalog with you in the palm of your hand.

Music easily moved from desktop, to laptop, to portable player, and a download could

not be scratched or lost (Plambeck, 2010).

Portability, the ability to listen to music anywhere and anytime, was the core

competitive dimension driving innovation in music playback media since the 1960s

(Gopinath & Stanyek, 2014). Looking at the LP, cassette, CD, and digital download, each

showed an immediate improvement in performance over its predecessor in regards to

portability, often at the expense of sound quality, price or depth of offering. Plambeck

(2010) stated, “In one way, the music business has been the victim of its own

technological success: the ease of loading songs onto a computer or an iPod has meant

that a generation of fans has happily traded fidelity for portability and convenience”

(paragraph 6).

Question 2: Was on-demand streaming initially inferior to existing music playback

formats in the core competitive dimension along which previous innovation had

occurred?

The core competitive dimension along which music playback formats historically

innovated was portability. Consumers continually favored and supported playback

options that provided the greatest freedom to take their music with them (Gopinath &

Stanyek, 2014). In examining on-demand music streaming, then, it was necessary to

determine the degree of portability of streaming services relative to the existing options at

the time, the most popular of which were CDs and digital downloads.

The first legal on-demand streaming platform to enter the US market was

Rhapsody, introduced by Listen.com in December of 2001. Rhapsody’s subscription-

based streaming platform offered consumers unlimited access to their catalog of songs,

which by 2002 consisted of over 175,000 titles, including popular tracks from every

major record label at the time, for $10 a month. However, users of the service could only

listen on a computer connected to the internet (Evangelista, 2002).

This restriction to online computer-based listening placed Rhapsody, as well as

other emergent on-demand streaming platforms such as Napster and Zune, at a

considerable disadvantage to CDs, the dominant playback format at the time. CDs were

compatible with home entertainment systems, portable players, car audio systems, and

computers whether they were online or offline (Coleman, 2003). As a result, on-demand

streaming was significantly inferior to CDs in the competitive dimension of portability.

Soon after the introduction of Rhapsody, Apple rolled out its digital download

store, iTunes, as well as the portable digital music player, the iPod (Apple, 2003). Digital

downloads, supported by low-cost digital music players from a variety of manufacturers,

offered greater portability than on-demand streaming or CDs. Digital downloads were

playable on portable digital players as well as computers, whether online or offline. In

addition, digital downloads were easily transferred from a computer to a portable device

and could be stored in multiple places at the same time (Coleman, 2003). On-demand

streaming, then, was also inferior to digital downloads in the competitive dimension of

portability.

Question 3: Did the mainstream music market initially reject on-demand music

streaming as a music playback format?

Initial response to on-demand music streaming was so modest, particularly

compared to the strong response to digital downloads, that Nielsen, the primary collector

and disseminator of market data for the entertainment industry, did not even start

reporting on-demand music streaming numbers until 2008, seven years after the launch of

Rhapsody. As shown in Table 4.2, in 2008, on-demand music streaming accounted for

less than .1% of total music consumption, generating only 352,755,259 streams or

235,170 stream equivalent albums (SEA) compared to an industry-wide 546,667,315

albums sold that year. Digital downloads for the same period, however, accounted for

31.5% of music consumption, generating 65,770,119 full digital albums and 106,939,845

track equivalent albums (TEA) (Nielsen, 2018).

Table 4.2

2008 Total Music Consumption by Format

2008 Raw Data 2008 Converted % of Total

Total Market (In Album Equivalents) 546,667,315 546,667,315

Physical Albums 373,722,181 373,722,181 68.36%

Digital Albums 65,770,119 65,770,119 12.03%

Digital Songs 1,069,398,449 106,939,845 19.56%

On-Demand Streams 352,755,259 235,170 0.04%

(Nielsen, 2018)

Figure 4.1 shows sales by format for the period 2008-2010. The regular spikes in

the chart represented seasonal increases in the month of December for each year.

Attempts to remove seasonal irregularities did not change the overall data, so it was

determined to leave all data in the chart.

There was a slight downward trend for the entire chart; however, the relative

position of the formats to one another remained unchanged for the period. The downward

trend was consistent with the overall decline of music consumption for the period

examined, as total consumption in 2008 was 546,667,315 album equivalents compared to

2010 consumption of 455,852,222 album equivalents, a decrease of 16.6% for the period.

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

A Pearson r correlation was run to determine if there was a significant

relationship between changes in on-demand streaming and the other formats including

CDs, download albums, and download songs between 2008 and 2010. Table 4.3 shows

the results of the analysis. There was no significant relationship between on-demand

streaming and any of the other formats.

Table 4.3

2008-2010 Correlation Analysis by Format

CDs DL Albums DL Songs Streams

CDs 1.00

DL Albums .28 1.00

DL Songs .32 .73 1.00

Streams -.17 .15 .08 1.00

N = 156

Question 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?

While on-demand music streaming services struggled to gain customers, they did

survive in spite of the lack of portability. While the numbers were very small, the overall

concept of paying for access to thousands of songs, as opposed to buying only a few,

appealed to a niche group of consumers, referred to by Bott (2010) as, “one of the

world’s smallest cults” (Paragraph 1). To answer whether or not on-demand music

streaming was a disruptive innovation required the researcher to determine the identity of

these consumers and why they chose to use on-demand streaming.

As outlined in Chapter Three, addressing question four required a four-step

process. First, the researcher created a genre-level willingness to pay continuum in order

to identify low-end consumers. Second, the researcher identified genres with the highest

number of non-consumers of music by establishing a ratio of paid to non-paid activity.

The next step was to identify which genre, if any, adopted on-demand streaming at a

faster rate than the overall market and look for any connection to genres identified as

having a low willingness to pay or a high percentage of non-music consumers. Finally,

the researcher examined archival documents for evidence of why early adopters might

have chosen to use on-demand music streaming.

To establish a willingness to pay continuum required the researcher to examine

sales by format at the genre level with the assumption that consumers within a genre with

a low percentage of full album activity relative to the overall market had a lower

willingness to pay, full albums costing far more than individual songs. In order to

neutralize the potential skewing effect of large blockbuster releases, the researcher

averaged data across the years 2008-2010. Table 4.4 shows percentage of consumption

by format for the entire market and the nine genres included in the study.

Average consumption for the total market between 2008 and 2010 provided a

baseline willingness to pay of 76.96%. Genres with a lower willingness to pay than the

total market included Pop (51.53%), R&B/Hip-Hop (73.45%), and Dance/Electronic

(68.08%). These three genres represented the low-end of the market as defined by

Schmidt and Druehl (2008) in their work on disruptive innovation.

Table 4.4

2008-2010 Average Consumption by Format

Full Albums Digital Songs Streaming

Total Market 76.96% 22.78% 0.26%

Pop 51.53% 47.90% 0.57%

R&B/Hip-Hop 73.45% 26.17% 0.38%

Rock 79.20% 20.53% 0.27%

Country 80.59% 19.22% 0.19%

Latin 88.77% 10.96% 0.27%

Christian/Gospel 86.10% 12.65% 0.11%

Jazz 88.92% 11.00% 0.09%

Dance/Electronic 68.08% 28.07% 0.31%

World Music 85.20% 14.65% 0.18%

(Nielsen, 2018)

Some disruptive innovations survive rejection by mainstream consumers by

creating a new market among people not currently participating in the existing market

(Christensen, 1997). In music, few people remain completely outside of the market;

however, many people opt not to pay for music, choosing instead to listen through free

options such as radio, programmed streaming, or illegal music download sites. For the

purpose of this study, these people represent non-consumers of music.

In addition to recording paid music consumption, Nielsen also tracks free activity,

including radio airplay and programmed streaming. Identifying genres with a low ratio of

paid activity to free activity is one way to isolate non-music consumers. Because radio

audience numbers (r) and programmed streaming numbers (ps) are several multiples

higher than consumption numbers (c), the formula c ÷ ((r ÷ 100) + (ps ÷ 100)) was used

to develop a paid-to-non-paid activity ratio. Table 4.5 shows the percentage of paid

activity relative to adjusted free activity resulting from the above formula.

Table 4.5

2008-2010 Paid-to-Non-Paid Ratio

Paid Consumption Airplay Audience Programmed Streams Ratio

Total Market 501,632,816 874,769,375,700 5,215,662,531 0.057

Pop 51,982,254 140,665,270,066 1,564,282,454 0.037

R&B/Hip-Hop 89,468,866 172,886,520,133 1,058,169,410 0.051

Rock 161,896,173 299,560,309,433 1,664,454,356 0.054

Country 52,690,740 113,678,967,266 597,768,313 0.046

Latin 19,212,333 60,601,299,566 135,199,298 0.032

Christian/Gospel 22,694,968 26,814,388,466 140,790,698 0.084

Jazz 11,183,407 8,690,875,933 180,792,281 0.126

Dance/Electronic 10,035,897 8,223,785,466 132,913,755 0.120

World Music 3,862,957 481,647,833 50,461,845 0.726

(Nielsen, 2018)

The baseline paid-to-non-paid ratio as determined by the total market was .057.

Genres with a lower paid-to-non-paid ratio included Latin, Pop, Country, R&B/Hip-Hop,

and Rock. These genres had more non-paid activity relative to the amount of paid activity

than the total market and, therefore, a higher number of non-consumers of music.

Table 4.6 shows the percentage of total consumption through on-demand music

streaming by genre. The benchmark for on-demand streaming as a percentage of total

consumption as determined by the average for the total market between 2008 and 2010

was .26%. Genres with a higher percentage of total consumption from streaming were

early adopters of on-demand music streaming and included Pop, R&B/Hip-Hop, and

Dance/Electronic.

Table 4.6

2008-2010 Ave % from Streaming

Streaming

Total Market 0.26%

Pop 0.57%

R&B/Hip-Hop 0.38%

Rock 0.27%

Country 0.19%

Latin 0.27%

Christian/Gospel 0.11%

Jazz 0.09%

Dance/Electronic 0.31%

World Music 0.18%

(Nielsen, 2018)

In comparing early adopters of on-demand music streaming with those genres

identified with the low-end of the market, there is perfect alignment with all three early

adopter genres. However, in comparing early adopters with the genres shown as having a

higher number of non-consumers, the connection is not as clear. At face value, this would

seem to indicate that early adopters of on-demand music streaming largely came from the

low-end of the existing market.

In examining why early adopters chose subscription services like Rhapsody, the

most common factor to appear was the subscription-based business model (Bott, 2010).

Early subscribers of on-demand streaming services could access tens of thousands of

songs for one monthly price. While traditional download sites such as iTunes offered

those same songs and more, users of those sites could only listen to those specific titles

they purchased. Access to all proved more appealing than ownership of some for these

early adopters (Gopinath & Stanyek, 2014).

Another factor mentioned in the literature regarding the appeal of early on-

demand streaming services was new music discovery, a factor of the competitive

dimension of depth of offering (Cesareo & Pastore, 2014). Curated playlists, user-

generated playlists, song recommendation features, and genre-based browsing allowed

users to discover new artists and songs outside of the relatively limited mainstream media

spotlight. Also mentioned was the fact that on-demand streaming more closely imitated

the experience offered by illegal streaming sites, providing a legal way for users to

continue browsing and trying music in their accustomed fashion absent the guilt of using

illegal sites (Cesareo & Pastore, 2014).

In the competitive dimension of sound quality, the average on-demand music

stream from sites like Rhapsody varied from 96 to 192 kbps depending on quality of

internet speed, configuration of the computer, and overall traffic on the site (Bullen,

2017). By comparison, CDs have always had a bit rate of 1,411 kbps (Coleman, 2003),

and the average digital download from iTunes has had a bit rate of 256 kbps (Sony,

2018). Clearly, on-demand streaming did not exhibit superior performance in sound

quality.

In looking at price, the radically different approach between a purchase and a

subscription model made price a difficult comparison because the models represent

different ways of viewing music consumption. The average music consumer in the US

spent $40 a year on music purchases in 2002 (Michel, 2006), whereas a Rhapsody

monthly subscription cost $120 per year. However, those subscribers were paying for

access to all of the songs on the platform, not just the content of three CDs, which is what

$40 would have purchased in 2002. It was a different model that appealed to a consumer

more concerned with access than ownership (Bott, 2010).

In summary, the appeal of the subscription-based business model combined with

superior performance in the competitive dimension of depth of offering appeared to drive

much of the early adoption of on-demand music streaming. In this instance, depth of

offering referred not just to the amount of content available, but also the degree to which

listeners could access the content. However, while early adopters loved the idea of access

over ownership, their numbers were few, prompting technology writer Ed Bott (2010) to

quip, “These services are still the stuff that cults are made of” (p. 3).

Question 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?

In the decade from 2001 until 2010, on-demand streaming languished on the

sidelines of recorded music with annual consumption never exceeding 1% of total music

consumption for the US market (Nielsen, 2018). Despite several services joining

Rhapsody in offering on-demand music streaming, most notably Napster (2005), Zune

(2008), and Rdio (2010), on-demand streaming remained relatively static, making only

incremental changes to the basic services first available in 2001 (Bott, 2010). Attempts to

make on-demand music streaming more portable faced major roadblocks, particularly

from Apple, whose portable digital music player, the iPod, had a dominant market share

among digital music players just short of 75% and a closed operating system, which

blocked the various streaming services (Delahunty, 2009).

The appearance of smartphones in the mid-2000s broke Apple’s chokehold on

music portability and allowed consumers more access to streaming websites through

mobile devices. Competition between the various on-demand streaming platforms,

though, kept any one platform from being able to offer access to all of the providers

(Bott, 2010). However, everything changed in 2011 with the introduction in the US of a

new on-demand streaming service, Spotify.

Spotify was a true second generation innovation entering the US market with a lot

of fanfare in July of 2011 with several distinct advantages over existing on-demand

music streaming platforms. Hailed by New York Times columnist Ben Sisario (2011) as,

“the world’s most celebrated new digital music service” (para. 3), Spotify entered the

market with significant financing and sophisticated partnership deals with all major

content owners (Catalano, 2018). In addition, Spotify was compatible with all of the

major smartphones, including the iPhone, and offered an impressive array of features that

drew the attention of mainstream consumers, such as increased portability, the ability to

incorporate downloaded files, offline caching of playlists, and, possibly the biggest

innovation, a free, ad-supported subscription option (Sutter, 2011).

Covering the Spotify launch for the New York Times, technology writer Sisario

(2011) wrote, “[Spotify’s] crucial selling point has been its free access, which the

company believes can lure in new users” (para. 11). Spotify’s belief was not just wishful

thinking. In Europe, between 2008 and 2011, Spotify had already signed up 10 million

users, 1.6 million of which were paying for a subscription (Sisario, 2011). The early free

version of Spotify had several restrictions, including limits on time spent listening, a cap

on the number of times a free user could listen to an individual song, and no mobile

access (Sisario, 2011). However, those restrictions were gradually removed so, that by

2014, even free users had unlimited access to Spotify’s entire catalog while on a

computer, and significant access through all mobile devices (Morris, 2014).

In a survey conducted in February 2018 by MusicWatch (2018), a company

dedicated to music industry market research and industry analysis, on-demand streaming

customers listed portability-related features as those most important to them as users.

Portability features mentioned by the majority of users included “easy to use in the car”

and “easy to use on my phone” (MusicWatch, 2018, p. 27). These responses seem to

indicate that improvements in on-demand streaming in the core competitive dimension of

portability reached the minimum threshold required by mainstream music consumers.

The free, ad-supported subscription model, combined with increased portability,

removed many of the barriers to on-demand streaming for mainstream users, leading to a

wave of experimentation. By August of 2011, only a month after their US launch, Spotify

reported 1.4 million US users with 175,000 of those using the paid version (Statista,

2012). Prior to Spotify’s launch, in January of 2011, services estimated the entire US

market for on-demand music streaming to be 1.5 million users (Bylin, 2011). This means

Spotify doubled the market for on-demand music streaming, accomplishing in one month

what it took multiple platforms combined almost a decade to achieve.

Spotify’s success was quickly followed by on-demand streaming platforms

introduced by Google Play Music (2011), Beats (2014), Tidal (2015), Apple Music

(2015), and Amazon Unlimited (2016). While each of these formats experimented with

free trial periods, only Spotify offered a permanently free subscription. However, all of

the services offered greater portability and mobility than the previous generation of on-

demand service providers.

Question 6: Did on-demand music streaming eventually encroach upon sales of

existing music playback formats resulting in a shift in the competitive landscape?

Figure 4.2 shows sales by format for the period of 2011-2017. As before, the

regular spikes of activity represented seasonal fluctuations around December. The

random spike in the middle of 2015 was due to a shift in reporting where Nielsen moved

the beginning and end reporting dates due to an industry-wide decision to release new

recordings on Fridays as opposed to Tuesdays, which resulted in one week (7/9/15) with

11 days of activity instead of 7. As noted earlier, attempts to remove seasonal fluctuations

did not materially affect the outcome of the study.

The data for 2011-2012 remained similar to that examined for 2008-2011 with

only a slight increase in streaming. However, in 2013, streaming activity took a

noticeable jump, and an exponential increase began in 2015 continuing through 2017. In

2011, on-demand streaming finished the year with 4,928,087 in stream equivalent albums

(SEA), which represented approximately 1% of total consumption for the year. By 2017,

on-demand streaming contributed 412,038,975 SEA, which accounted for 64.72% of total

consumption for the year. Over that same period, overall consumption of music grew

from 471,591,738 total consumption in 2011 to 636,633,549 in total consumption in

2017, a 35% increase during the period.

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

A Pearson r correlation analysis was run to determine if these changes in on-

demand streaming and corresponding changes to the other formats represented a

significant relationship. Table 4.7 shows the results of the analysis. Unlike the 2008-2010

analysis, this time there was a significant negative relationship between changes in on-

demand streaming and the other formats.

The strongest negative relationship was between on-demand streaming and

downloaded songs, r (362) = -.83, p < .001. This was consistent with the raw data, which

showed digital songs, or track equivalent albums (TEA) dropping from 127,111,330 TEA

in 2011 to 55,481,541 TEA in 2017, a 56.4% decrease over the period. This was also

consistent with the findings in question four, which presented evidence that the fastest

adopters of on-demand music streaming were those with a lower willingness to pay.

Table 4.7

2011-2017 Correlation Analysis by Format

CDs DL Albums DL Songs Streams

CDs 1.00

DL Albums .58 1.00

DL Songs .50 .84 1.00

Streams -.49* -.67* -.83* 1.00

*p < .001

N = 364

Source: (Nielsen, 2019).

The next strongest negative relationship was between on-demand streaming and

downloaded albums, r (362) = -.67, p < .001. This was also consistent with the raw data,

which showed digital albums dropping from 103,091,988 albums in 2011 to 66,212,591

albums in 2017, a 35.7% decrease over the period. Overall, digital downloads in the form

of songs and albums dropped from a combined share of 48.8% of total consumption of

2011 to a combined share of only 19.11% in 2017.

While the negative relationship between on-demand streaming and CDs was

moderate when compared to downloaded songs and albums, it was still a significant

relationship, r (362) = -.49, p < .001. CDs dropped from 236,460,333 in 2011 to

102,900,442 in 2017, a 56.4% drop over the period. Again, this is consistent with the

findings of question four where consumers with the highest willingness to pay were

slowest to adopt on-demand music streaming.

Evidence is very strong that on-demand streaming encroached heavily upon

existing formats, going from 1.04% of total consumption in 2011 to 64.72% of total

consumption in 2017. However, the growth in streaming also resulted in a 35% increase

in overall consumption of music during the period, offsetting a trend of decline that dated

back to the mid-2000s. This would seem to indicate that streaming did not just shift

consumers from one format to another, but actually brought in a significant number of

non-consumers from non-paid platforms.

Summary of Findings

The quantitative and qualitative data gathered from archival sales records and

historical documents related to the introduction and rise of on-demand streaming appear

to confirm that on-demand music streaming was in fact a disruptive innovation. A careful

comparison of Christensen’s (1997) theory side-by-side with a summary of the facts

illustrates that the theory correctly predicted the path on-demand music streams would

take. A detailed comparison follows.

According to Christensen’s (1997) theory, the first characteristic of a disruptive

innovation is that it is initially inferior to existing options in the core competitive

dimension preferred by the market. A survey of the history of music playback formats

shows that consumers have consistently preferred portability over any other competitive

dimension in music (Gopinath & Stanyek, 2014). Likewise, documents related to the

launch of on-demand music streaming, most notably the launch of Rhapsody in 2001,

demonstrate that early versions of the format only allowed consumers to stream music

from a computer connected to the internet (Sisario, 2011). This restriction to online

computers meant early services provided far less portability than either CDs or digital

downloads, both of which played on mobile devices, in cars, and on offline computers.

According to the theory, because the innovation is inferior in the core competitive

dimension, the mainstream consumers of the market reject it (Christensen, 1997). An

examination of archived sales records related to on-demand music streaming make it

clear the mainstream market soundly rejected on-demand streaming in its initial form.

Even though on-demand streaming was available as early as 2001, consumer activity was

so small that Nielsen, the primary collector and distributor of entertainment industry

activity, did not even start tracking on-demand streaming numbers until 2008. At that

time, almost a decade after the introduction of on-demand streaming, consumer streaming

accounted for only .1% of music activity in the United States.

The third key characteristic of a disruptive innovation is that, in spite of its

rejection by the mainstream market due to its inferiority in the core competitive

dimension, the innovation survives because it appeals to the low-end of the existing

market or a new market through superiority in a secondary competitive dimension or

through a unique business model (Christensen, 1997). On-demand music streaming did

survive, largely due to its unique subscription business model, which allowed access to

all songs as opposed to ownership of some (Bott, 2010). Quantitative analysis of early

on-demand streaming numbers from 2008-2010, sorted by genre and organized into a

willingness-to-pay continuum showed early adopters to have come from the low-end of

the existing market, namely the Pop, R&B/Hip-Hop, and Dance/Electronic genres

(Nielsen, 2018, Schmidt & Druehl, 2008). Qualitative analysis of historical documents

from the period showed early adopters preferred the depth of offering and unique

subscription-based business model offered by on-demand streaming sites (Bott, 2010).

The theory of disruptive innovations states that, over time, the innovation, which

has managed to find a small following, begins to improve in the core competitive

dimension while also maintaining its advantages in other dimensions or through its

unique business model (Christensen, 1997). Starting in 2011, with the launch of Spotify

in the US, on-demand music streaming rapidly improved in the core competitive

dimension of portability. Through unique licensing arrangements with content owners,

stronger broadband internet support, and deals with all of the major mobile carriers

including Apple, Spotify was a true second-wave innovation (Sisario, 2011). In addition

to improved portability, Spotify not only continued the innovative subscription-based

business model introduced by Rhapsody a decade earlier, but improved upon it by adding

a free version (Sisario, 2011). Free access to millions of songs proved tempting enough to

entice trial from mainstream music consumers with 1.4 million users signing up within

the first month of Spotify’s launch (Statista, 2012).

The final characteristic of a disruptive innovation is that it eventually encroaches

upon existing options (Christensen, 1997; Schmidt & Druehl, 2008). In the first decade of

on-demand music streaming, from 2001-2010, the innovative format, championed by

Rhapsody, had no effect on the existing formats of CDs or digital downloads, never

contributing even 1% of total music consumption at any point in the period (Nielsen,

2018). However, with the launch of Spotify in 2011, the on-demand streaming format

began to grow rapidly so that by the end of 2017, on-demand streaming accounted for

65% of total music consumption (Nielsen, 2018). During that same period of 2011-2017,

CDs fell from 50% of total consumption to 16% of consumption and digital downloads

dropped from 49% of total consumption to 19% of consumption; a fall from which

neither is likely to recover.

An old idiom states, “A picture paints a thousand words,” which bears true in this

instance. Figure 4.3 demonstrates a classic disruptive innovation pattern with on-demand

streaming going from a flat-line trend from 2008 through 2012, to gradual upward

movement starting in 2013, followed by a steeply rising curve in 2015, accompanied by

dramatic declines in all other formats. On-demand music streaming moved from 1.04%

of total consumption to 65% of total consumption in the US in the seven-year period of

2011-2017 after practically no growth in the prior decade. It appears to have been a

classic example of Christensen’s (1997) theory of disruptive innovation.

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

Limitations

As with any research project, the present study had several limitations, some of

which may have had an impact on the degree to which the data represented in the study

reflected the actions of the market. First, two variables had the potential to produce a

confounding effect on the results of the study. However, the actual effect of these

variables, in reference to the actual question as to whether on-demand music streaming

was a disruptive innovation, was negligible as is explained below. In addition, the lack of

prior research on the adoption of music formats in the US presented limitations as well.

Finally, the lack of data related to individual, consumer-level behavior led to the less

reliable use of genre-level data. A brief discussion of these limitations follows.

One potential confounding variable in this study was access to the internet speeds

required for on-demand music streaming. On-demand music streaming sites

recommended internet speeds of 256 kilobytes per second (kps) (Dilley, 2017). In the

early 2000s, when Rhapsody first introduced on-demand music streaming, roughly 92%

of internet-using households were still accessing the internet through a dial-up modem

with average speeds of 56 kbs (Kleinbard, 2000). Lack of access to the necessary internet

speeds for on-demand streaming could have been a variable in the early rejection of the

format. This limitation was largely offset, however, by the fact that in the decade between

2001 and 2010, access to broadband, high-speed internet as well as mobile web access,

referred to as 3G, exponentially increased (Statista, 2019), and yet adoption of on-

demand music streaming remained negligible (Nielsen, 2018). For this reason, the

researcher opted not to include an in-depth examination of internet usage as part of this

study. Certainly, lack of access to adequate internet speeds could have been a factor in

the initial rejection of on-demand music streaming, however, if it had been a primary

factor, adoption would have increased along with increasing internet speeds, and that did

not occur.

Another potential confounding variable was the presence of illegal music

streaming websites as a substitute for legal music playback formats. A major part of this

study involved the interplay between the various music playback formats, most notably

CDs, digital downloads, and on-demand streaming. The presence of illegal substitutes for

these formats influenced consumer behavior, but in a manner hard to measure, as such

activity lay outside of the visible market. While several organizations, most notably the

Recording Industry Association of America (RIAA), have attempted to quantify the use

of illegal online music options, in the opinion of the researcher, they failed to isolate the

impact of illegal activity to the degree that the data could have been used in this study

(Siwek, 2007). As a result, the researcher limited this study to legal formats only.

The absence of illegal activity in the numbers related to the adoption of on-

demand music streaming potentially skewed the overall picture related to the rate at

which consumers adopted on-demand streaming. Research offered by Spotify (Spotify,

2013) indicated a decline in the use of illegal websites because of consumers choosing to

use Spotify instead. For the most part, though, such considerations fell outside the scope

of the question as to whether on-demand streaming constituted a disruptive innovation.

Examination of the activity on paid platforms more than adequately addressed the overall

question of the study and the inclusion of data related to illegal activity or programmed

streaming would not have changed the outcome, only the degree of interplay among CDs,

digital downloads, and streaming.

Another limitation of the present study was the lack of prior research related to

the adoption of on-demand streaming. As a relatively recent phenomenon, not enough

time has passed for studies on on-demand streaming to make their way through academic

channels. This required the researcher to rely on non-academic sources such as historical

press reports, news articles, corporate press releases and other primary sources. I hope

that this study has been able to introduce data related to on-demand music streaming in

such a manner as to stimulate academic conversation around the issue and aid future

researchers.

One last limitation was the fact that consumer behavior in relation to music was

non-exclusive and, as a result, largely hidden. The same consumer could purchase a CD,

download a song, and listen to a stream all at once, not to mention listen to the radio,

stream from a programmed site or even use an illegal website. A consumer choosing one

format did not necessarily do so at the expense of another format. This lack of exclusivity

made it difficult to isolate consumer behavior and preference. As has been explained,

because of this lack of transparency of individual consumer behavior, the common

practice in the music industry has been to use genre as a means of examining consumer

behavior (Lena & Peterson, 2008). However, genre preference itself was not mutually

exclusive; a consumer could easily be a fan of Pop and Country music at the same time.

Using genre as a means of isolating consumer behavior was the best option available, but

less than ideal. Future researchers may consider taking the time to develop longitudinal

studies of consumer preference and behavior at the individual consumer level.

Implications and Recommendations

Economic theorist, Joseph Schumpeter, referred to innovation as a process of

creative destruction, a necessary displacement of the old in order to bring about growth

and health within a market (Mee, 2009). Prior to the rise of on-demand music streaming

as the leading music playback format, the US music industry had experienced a fourteen-

year decline in revenue going from a peak of $14.38 billion in 2000 to a low of $6.695

billion in 2014 (RIAA, 2017). While the move from a purchase model to a subscription

model brought about by on-demand music streaming was quite disruptive for music

industry stakeholders, the result has been a major market turnaround resulting in $8.723

billion in revenues in 2017, a 30% increase over the low point of 2014.

The music industry took a risk by negotiating licenses with Spotify that allowed

them to offer a free, ad-supported subscription. The data examined in this study would

seem to indicate, though, that the model of using free subscriptions to move people into

paid subscriptions not only paid off, but also led to a dramatic recovery for recorded

music. Disruptive innovations can be painful, but they are often necessary for an industry

to survive.

Not only have recorded music revenues grown, several of the champions of on-

demand music streaming have claimed that this growth has been at the expense of illegal

online music activity. According to Spotify’s website, there has been a dramatic decrease

in illegal online activity since Spotify began operations in the US (Spotify, 2013). Their

research shows that young people aged 18-29 are 55% less likely to use illegal file

sharing when offered a free legal alternative (Spotify, 2013). Future research should

concentrate on validating these claims.

One interesting pattern that emerged through the research was the fact that not all

genres adopted on-demand streaming at the same rate. Table 4.8 shows on-demand

streaming as a percent of total consumption for the years 2011 and 2017 by genre. The

figure also shows the market share for each genre for the two periods.

Table 4.8

Growth in Streaming and Market Share by Genre: 2011 and 2017

2011 Stream % 2011 Market Share 2017 Stream % 2017 Market Share

Total Market 1.04% 100.00% 64.72% 100.00%

Pop 1.86% 13.60% 65.86% 12.69%

R&B/Hip-Hop 1.76% 17.20% 76.73% 24.56%

Rock 0.63% 29.00% 47.05% 20.77%

Country 0.74% 11.70% 46.40% 7.70%

Latin 1.35% 2.95% 88.41% 5.87%

Christian/Gospel 0.17% 4.49% 43.99% 2.65%

Jazz 0.23% 1.93% 38.83% 1.03%

Dance/Electronic 1.17% 2.70% 76.92% 3.67%

World Music 0.32% 0.65% 72.63% 1.00%

(Nielsen, 2018)

Genres who adopted streaming at a rate significantly faster than the total market,

indicated in italics in Figure 4.6, showed market share growth between 2011 and 2017.

Of particular note was the growth of R&B/Hip-Hop from 17.2% market share to 24.56%

market share during the period examined. Not only did R&B/Hip-Hop grow in market

share, it grew in raw numbers going from 81,115,806 in total consumption in 2011 to

156,386,485 total consumption in 2017, a 93% increase over the period.

Similarly, those genres that adopted at a rate significantly slower than the overall

market experienced a decline in market share. Most notable among this group was Rock,

which went from 29% of the total market to 20.77% of the total market. Country music

also experienced a significant decline in market share during the period dropping from

11.7% to 7.7% during the period.

Future research into on-demand streaming should examine the connection

between early adoption of on-demand streaming and growth in market share. The

substantial growth in total volume among early adopting genres would indicate the

change in market share was due to more than a shift in consumer taste. The data would

seem to indicate that those genres were able to grow by moving non-paying consumers

into payed services. This would be worth future study and particularly useful for genres

that have experienced declines during the period.

Another interesting pattern that emerged was the lack of seasonality in the

streaming data. As noted in the various sales charts, particularly Figure 4.3, there was a

spike in activity each year in the month of December. This spike in activity was most

noticeable in the line indicating physical album purchases.

In examining the other formats, the seasonal trend was less remarkable among

digital downloads and entirely absent in the on-demand streams. This absence of

seasonality was an important element in understanding the shift from purchase to

subscription behavior. The lines indicating purchase behavior, including physical albums,

digital albums, and digital songs, are measuring a single transaction where a consumer

purchases a piece of product. The seasonality is a result of retail patterns because the

chart is measuring purchase activity.

In on-demand streaming, the transaction represented by the chart was not a one-

time purchase, but an individual stream of a song; it reflects listening behavior, not

purchase behavior. The lack of seasonality, then, was due to the measurement of listening

activity, which does not fluctuate greatly based on seasons. The lack of seasonality in on-

demand music streaming activity has interesting implications and is worth future study.

One last observation from the data was that digital downloads, once expected to

become the dominant music playback format (Coleman, 2003), never actually displaced

the CD. According to the Nielsen (2018) data, digital downloads reached their peak in

2012, almost a decade after the launch of the iTunes store, with full album downloads

generating 117,582,197 in consumption, and single song downloads generating

133,588,041 in TEA for a combined 53.4% of total consumption. That same year, CDs

alone sold 206,752,704 copies, representing 43.97% of total consumption. Digital

downloads encroached upon, but never replaced CDs. On-demand streaming, though, has

displaced CDs to the point that, by 2017, CDs only made up 16% of total consumption.

Digital downloads appear to have been a bridge technology leading from the

physical CD format to the on-demand music stream. Digital downloads represented a

comfortable transition for a music industry and consumer base used to the concept of

owning music and a business model built on purchases. In addition, 2001 copyright law,

internet speeds, and mobile hardware were simply not ready to provide the on-demand

streaming experience most mainstream consumers expected. It would take a decade for

the stage to be set for on-demand streaming to thrive.

The idea of technological innovations that are only partial moves into the digital

space is not restricted to music. Netflix started with distribution of DVDs and moved to

on-demand streams. Amazon started out as an online book distributor and became a

distributor of online books, and then a distributor of everything. In future studies on

disruptive innovations, it would be useful to look at this phenomenon of second wave

innovations that take advantage of fully developed technology not available at the time of

an initial innovation. There could be industries that believe they have fully transitioned

into the online space that are actually in a bridge period with an even more disruptive

innovation waiting in the wings.

Twenty years after first proposing the model that would form the basis for

disruptive innovation theory, Clayton Christensen, along with Michael Raynor and Rory

McDonald (2015), warned in a Harvard Business Review article that incumbent firms

must, “Disrupt or be disrupted” (p. 8). For industry leaders to continue to lead, they must

become better at innovation. However, as Schmidt and Druehl (2008) point out,

“sustaining innovations have more often been associated with incumbents and disruptive

innovations with entrants” (p. 349).

After a decade and a half of decline, the music industry experienced a turnaround.

A major part of that shift in momentum was due to the industry finally recognizing the

need to embrace new music formats and the subscription business model. This required

rethinking music licenses, copyright payments, and a shift from ownership to access in

regards to music.

While innovation around on-demand streaming involved several entrants, most

notably, Rhapsody, Spotify, and Amazon, many incumbent firms were able to make the

transition into on-demand music streaming, including major record labels, publishers,

artists, songwriters, and other content owners. They did this by embracing innovation,

creating partnerships that took advantage of new revenue streams, and imagining new

ways of doing business. Disruption does not always have to lead to destruction.

Christensen, Raynor, and McDonald (2015) state at the end of their Harvard

Business Review article, “As an ever-growing community of researchers and practitioners

continues to build on disruption theory and integrate it with other perspectives, we will

come to an even better understanding of what helps firms innovate successfully” (p. 11).

This study of on-demand music streaming offered yet another look at a successful

disruption, one in which incumbent firms survived for the most part. Perhaps this is

evidence that Christensen’s work has made a difference in helping businesses cope with

disruption and harness its creative force for growth.

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