the longer tails of itunes, pandora, and youtube

The Longer Tails of iTunes, Pandora, and YouTube:

New Technology Shaping Music Preference and Spending

Andrew D. Penrose

Program in Science, Technology, and Society

Stanford University

The author wishes to thank his advisors Professor Robert McGinn and Professor David

Voelker at Stanford for their valued feedback and guidance on this project. Additional

thanks to all survey respondents, interview volunteers, Professor Fred Turner for the

lecture that inspired this study, and all others who contributed support.

Correspondence concerning this paper can be sent to Andrew Penrose, 675 Lomita Drive,

Stanford, CA 94305. Address email to [email protected].

The Longer Tails of iTunes, Pandora and YouTube Penrose

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Abstract

While the limited bandwidth of FM radio facilitated widespread adoption of mainstream

music preferences and spending habits, new digital music technologies recommend and

feature music based on personalized user profile data. Whether this includes tracking

purchase history, song “likes”, users’ emotions, or otherwise, the shift from majority-‐based

music recommendation to individual-‐based is a recent and relatively unexplored

development in the music industry. The purpose of this study is two-‐fold: to determine the

most influential factors shaping users’ choice of music technology, and the extent to which

these new technologies affect music preferences, spending and engagement. Focusing on

iTunes, Pandora, and YouTube, purpose-‐built surveys examine the reasons users choose

each service and how they perceive the technologies have affected their music

consumption. Additional survey questions seek patterns and correlations between

demographics, musical experience, music preferences, and music listening environment.

125 college students voluntarily completed the survey, revealing strong correlations

between variables currently ignored by music recommendation technology. By enhancing

our understanding of how new music technologies impact individual users, this study may

guide how music applications can improve user profiling, personalization, and the user’s

music-‐listening experience as a whole.

Keywords: digital music technology, the Long Tail, music preferences, profiling, multivariate music recommendation, iTunes, Pandora, YouTube, internet radio


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Table of Contents

INTRODUCTION (3-4)

LITERATURE REVIEW (4-10) THE LONG TAIL: NEW TECHNOLOGY REVEALING NICHE PREFERENCES MUSIC PREFERENCE STUDIES

METHODS (10-16) SAMPLING CONCEPTS Demographics Musical Experience Music Preferences Listening Environment Music Service Features and Effects

CODING AND DATA ANALYSIS RESPONDENTS

RESULTS (16-60) MUSIC PREFERENCE AND DETERMINING FACTORS Song Preference Genre Preference Determining Factors in Music Preference

Correlations Between Genres Demographics and Genre Preferences Musical Experience and Genre Preferences Listening Environment and Genre Preferences

MUSIC TECHNOLOGY PREFERENCE AND DETERMINING FACTORS Factors in Music Technology Preference

Favorite Feature Demographics and Music Technology Preference Musical Experience and Music Technology Preference Music Preference and Music Technology Preference

MUSIC TECHNOLOGY INFLUENCING PREFERENCE AND SPENDING Effects on Music Preference

Listen More Wider Range of Genres Deeper Within Familiar Genres More Sharing Music

Effects on Spending More Buying Buying Different Music Buying Concert Tickets Music is Bigger

DISCUSSION (61-65)

REFERENCES (66)

APPENDIX (67-75)


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Introduction

It’s your last high school gym class before moving to college. You return to your

locker to find the lock broken and someone has stolen your iPod and entire music

collection out of your backpack. Fearing you may turn to a life of digital piracy and cyber

crime, your parents purchase the new 32GB iPod Touch that holds 7,000 songs and

connects to the Internet. Having lost all of your music, you research some popular music

applications. It’s May 20, 2012 and as of April 30th, the iTunes store offered over 28 million

songs. How do you choose which 0.025% to buy? Do you instead rely on the endless stream

of YouTube videos your friends share on Facebook, or do you create a Pandora station like

the 150 million other Americans that enjoy personalized music recommendations?

The limited bandwidth of AM/FM radio necessitated a popularity contest for songs,

but the technical constraints of terrestrial radio don’t apply to digital music. The

combination of nearly unlimited music choice and a wide variety of music sites make

modern music experiences vastly more personal than terrestrial radio. The proliferation of

song recommendations, shared playlists, and music blogs attest to the power of the digital

music experience.

After a particularly inspiring lecture on digital media by Professor Fred Turner last

year, I designed and conducted a survey on Pandora use for a Communication course at

Stanford. Asking 112 respondents if they had ever bought an unfamiliar song after hearing

it only once on Pandora, 59 students equaling 53% of the sample indicated that they had.

Even more surprising, 15 students (13%) indicated they had bought an entire album after


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hearing a song from it for the first time on Pandora. Although I recognized that the Stanford

students that made up the survey sample were not representative of Pandora’s entire user

base, it seemed likely that a significant percentage of Pandora’s users were purchasing

unfamiliar music as well. I wanted to know how these new transactions would affect the

music industry, amidst declining sales and a torrent of illegal filesharing applications.

Literature Review

The Long Tail of Digital Music: New Technology Revealing Niche Preferences

After Chris Anderson published his article “The Long Tail” in Wired magazine in

October of 2004, it quickly became the most cited article in Wired’s history, and his book

became one of the most influential business books of the decade (Anderson). Using e-‐

commerce data that had been historically restricted to executives, the book outlines

Anderson’s theory that the Internet has expanded the range of effective inventory from a

limited number of “hits”, as seen on WalMart and Blockbuster shelves, to nearly infinity.

Since the post-‐WWII era of TV and

radio, businesses have traditionally

capitalized on the power of the top

100 or even 100,000 mainstream

products, ignoring all the books,

songs, and goods that didn’t make

the charts (Figure 1). But as both

Figure 1


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Anderson and Lessig point out, the recent success stories of Amazon, Netflix, and iTunes

prove that the companies providing customers with the most choices and the most

effective ways to navigate them can earn as much as 40% of their revenue from products

along “the long tail.”

During the age of FM radio and record stores, limited inventory and constrained

choice contributed to widespread adoption of popularized music preferences. Retailers

optimizing limited shelf space and FM radio DJs seeking to maximize listenership

reinforced a culture-‐wide fascination with top charts and superstars. However, as digital

music technologies continue to proliferate, the seemingly unlimited number of musical

choices and their innovative recommendation systems are shaping listeners’ preferences

and consumption patterns in new ways. Although the possible ramifications of unlimited

choice and user profiling are numerous, I expect these technologies to both widen and

deepen the music preferences of their users. In other words, the unique features of new

music services will not only enable the tracking of the Long Tail, but also shift demand to

make it even longer. The purpose of this study is two-‐fold: to determine the most salient

factors that shape listeners’ music preferences and choice of music service, and to enhance

our understanding of new music technologies’ impact on users.

Throughout history, from Mary Shelley’s Frankenstein to George Orwell’s Nineteen

Eighty-Four, the idea of technological determinism has caused society to irrationally view

and fear technology as an autonomous juggernaut, sometimes causing the restriction of

tools that extend humanity’s potential (McGinn). A technological determinist might use

phrases like identity theft, violation of privacy, and entertainment piracy to describe the


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Internet’s relationship with its users. In his book Remix, Lessig argues that the digitization

of culture and the economy is a positive change to be embraced and understood, rather

than restricted and criminalized. He protests against outdated copyright laws now

criminalizing creative actions, calling for copyright law reform to realize the full potential

of the new hybrid of commercial and sharing economies. After detailing both economies

individually, he argues that the Internet’s new hybrid economy is a fusion of both voluntary

collaboration and traditional commerce. He provides several examples of companies —

including Netflix, Amazon, Google, YouTube, and Second Life — and mechanisms, such as

user reviews and recommendations, crowdsourcing, and Anderson’s Long Tail principle,

that support his argument that the new hybrid economy is “a model of success, not a

compromise of profit.” McGinn also testifies to the vital importance of resisting

technological determinism, acknowledging technology and society as interdependent and

co-‐evolutionary, and monitoring the unique powers associated with each. These ideas

guided this study throughout the various stages of literature review, data collection, and

analysis.

Contrary to technological deterministic perspectives, more and more IT-‐based

media channels and corporations are capitalizing on their control over technology to shape

user interactions online. Amazon’s book recommendation feature is one example of a

navigational tool intended to both maximize profit and cater to users’ preferences. As

Anderson points out in the first chapter of The Long Tail, Amazon’s pairing of the best seller

Into Thin Air with the lesser-‐known Touching the Void via its recommendation feature

created a powerful positive feedback loop of both interest and revenue. By categorizing

media based on similarity, rather than — or in addition to — listing them by popularity,


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these institutions and corporations better serve both the user and the long tail of the

market.

In his book The Wisdom of the Crowds, author James Surowiecki explores the notion

that a large group of people is more innovative and better at problem solving than a small

elite creative team, concluding that this technique of “crowdsourcing” has enormous

potential and has already begun to shape online interaction (Surowiecki). Taking

Surowiecki’s advice, the popular online DVD rental service Netflix conducted a nearly

three-‐year-‐long public competition for an improved Netflix recommendation algorithm,

making Netflix usage data freely available in an effort “to substantially improve the

accuracy of predictions about how much someone is going to enjoy a movie based on their

movie preferences” (http://www.netflixprize.com). The winning team’s algorithm is yet

another user-‐centered tool used to connect niche market products and media to their

customers, directly facilitating the expansion of the long tail.

Studying Music Preference

Many researchers have conducted studies revealing correlations between

demographical information, such as age, gender and education, and music preferences.

LeBlanc et al. created an overall music preference index to measure subjects’ total

preferences across genres and compared responses between different age groups. After

surveying 2,262 respondents, the researchers found that the music preference index

declined in elementary students, rose from high school to college, and declined after college

(LeBlanc et al., 1996). While these findings may not provide a means to improve music

recommendation algorithms, statistically significant correlations between age and


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preferences for specific songs, artists, and genres would certainly help predict listener

reactions. Although surveys may be able to determine linkages between age and genre

preferences (as this study will show), this method is obviously not feasible for collecting

larger data sets regarding artist or song preference. However, this is one of many examples

of how digital media’s growing trend of “thumb” or “like” feedback could be utilized by

companies like iTunes, Pandora, and YouTube.

While most music recommendation sites focus on users’ preferences and musical

similarities between songs, several Taiwanese researchers (Suh-‐Yin Lee et al., 2009)

investigated the use of emotion-‐based music discovery within the context of motion picture

scores. Constructing an original algorithm called the Music Affinity Graph-‐Plus, Suh-‐Yin

Lee et al. achieved an impressive 85% accuracy in matching queried emotions with music

of the same emotions. While these results and the growth of music recommendation sites

like Stereomood and Music for Emotion prove the potential of emotion-‐based song sorting

and recommendation, such an approach has yet to draw a fraction of the audience of

iTunes, Pandora or YouTube. In acknowledgement of its potential, this study will also

survey respondents on their level of demand for emotion-‐based music recommendation.

In 2009, Gaffney and Rafferty conducted a study investigating users’ knowledge and

use of social networking sites and folksonomies (user-‐generated taxonomies), focusing on

the potential of social tagging to aid in the discovery of independent music. Examining the

four music discovery sites MySpace, Lastfm, Pandora and Allmusic through user surveys

and interviews, they found that although respondents use social networking sites for music

discovery, they are generally unaware of folksonomic approaches to music discovery.


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Furthermore, those who do use and contribute to folksonomies are mostly self-‐serving in

their motives (Gaffney and Rafferty, 2009). While Gaffney and Rafferty state that their

study rests upon the assumption that music recommendation and social networking sites

push users and revenue toward the Long Tail, they make no attempt to quantify the impact

of any particular site on the time or money users spend on Long Tail songs. Additionally,

the landscape of music discovery sites has changed dramatically since they conducted the

study, especially in the case of Pandora’s rapid growth.

Unfortunately, the vast majority of studies involving music preferences use a

nomothetic approach to choose one or two particular factors to test, whether for simplicity

or convenience. Christenson and Peterson built upon earlier studies of gender and music

genre preferences by including many “metagenres” previously disregarded by social

scientists. Consistent with similar studies, they found convincing evidence that gender

predisposes people to certain music preferences; for example, that females gravitate

toward popular music and males gravitate away from it. While this study contributes a

piece of the music preference-‐mapping puzzle, Christenson and Peterson admit, “the

underlying structure of music preference cannot be accounted for by reference to two or

three factors, but is multivariate” (Christenson et al, 1988). At this point, the need for an

idiographic approach to music preferences is clear.

This study is partially driven by the lack of a multivariate or idiographic study

comparing the relative impacts of age, emotion, social network, choice of digital music

service, and more factors, on music preference. iTunes, Pandora and YouTube certainly

have a wealth of data on their services’ use and users, but data points like relative


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preference between services, musical education and experience of users, and listening

environment are often ignored. Not only do I find this information intriguing, I suspect it

could prove incredibly relevant to both music marketing strategies and music

recommendation technology. In addition to enabling the examination of underlying

patterns between these variables, the collected surveys provide a basis for predicting

economic shifts in the music industry. It is expected that by aligning recommendations

with each unique users’ profile rather than the most popular songs, new music

technologies like iTunes, Pandora, and YouTube both please users and support more artists

further down the Long Tail. Furthermore, the findings presented in this study reveal

significant relationships between variables that have thus far been excluded from music

recommendation algorithms.

Methods

Sampling

Given my interest in the college student demographic and my immediate network of

friends and family, I focused my recruiting efforts on three different colleges: Stanford

University, Glendale Community College (GCC), and Arizona State University (ASU).

Stanford was the first and most convenient sampling frame for me as a Stanford undergrad,

providing 38 respondents. My parents, both professors at Glendale Community College,

invited their students to take the survey and added 71 students to the sample. Last, I sent a

brief Facebook message to recruit ASU students from my high school network. Response

and completion rates were lowest at ASU, with 9 students completing the survey. The


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shortage of both time and funded incentives ruled out a random sampling of college

students, but I minimized potential biases by recruiting from several different schools.

While a realistic distribution between schools would have been preferable, the number of

college students who volunteered for my unpaid, 20-‐minute survey was significantly higher

than I anticipated.

Concepts

The first page of the survey addressed respondents’ demographics, including age,

gender, hometown, current school, and competence with computers. Free response, or

open-‐ended, answer formats will be used for age, hometown, and current school, while

gender and computer competence will use closed-‐ended questions. The question “Please

categorize your competence using computers” will include the options “Advanced”,

“Average”, “Basic”, and “None\Very Little.” These items were carefully chosen for clarity

and appropriateness, to ensure optimal accuracy. The demographic variables were chosen

for potential to influence both music preference and music technology preference.

The second page of the questionnaire features units of analysis addressing

respondents’ musical experience, in order to gauge how each influences music preference.

Each concept will contribute to an index summarizing overall musical experience, assigning

quantitative values to qualitative responses where appropriate. First, subjects were asked

the open-‐ended question “Approximately how many hours per week do you spend listening

to music?” Next, respondents selected the option, “Which best describes the frequency of

your online music listening?” from the list: “Rarely”, “Sometimes”, “Often”, and “All the

Time.” Then, using a check-‐all question format, respondents indicated the school years


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during which they took at least one music class, with the options “Elementary (K-‐8th)”,

“High School”, “College”, and “None”. Next, subjects indicated how many years they have

taken musical instrument lessons (outside of school) with free response. Finally, the

closed-‐ended question “Do you currently play an instrument?” was followed by the

contingency question of “How many years have you played an instrument?” In order to

maintain both accuracy and the respondent’s attention, these questions and question

formats were chosen based on their clarity, relevance, and brevity for each unit of analysis.

Both the index and individual units of analysis will be used in determining the most salient

factors in music preference.

The next page of the survey investigated subjects’ music preferences. For the

purposes of this study, music preferences were defined as genres that an individual simply

enjoys listening to. As mentioned earlier, genres are the most feasible unit of analysis for

music preferences using a survey, given the large numbers of artists and songs in existence.

Using a matrix question, participants were asked, “What are your attitudes toward the

following music genres?” In addition to operationalizing this concept with multiple levels

of enjoyment (dislike, neutral, like, and love), the list of genres included those common

throughout all three music services in question (see Appendix for full survey). The primary

issue carefully controlled in this question was the respondent’s understanding of music

genres. For this reason, the selected genres were pragmatically selected for distinctness

from one another. While this potential confound has been minimized, it cannot be fully

eliminated without including potentially distracting full definitions of each genre.


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Although the varying levels of preference within genres, or the “depth” of music

preference, have been accounted for with the four options listed on this genre, the

following questions utilized a different approach to measure the same concept. After

subjects indicated their favorite genre from the same list, they were asked, “Within your

favorite musical genre, approximately what percentage of artists and songs that you know

do you like?” with the options “0-‐20%”, “21-‐40%”, “41-‐60%”, “61-‐80%”, and “81-‐100%”.

Next, the questionnaire asked the closed-‐ended question, “Of all the “top 40” popular music

you’ve heard, you like:” where subjects chose between “All or almost all”, “Most”, “About

half”, “Some”, “None”, and “I don’t pay attention to top 40 charts”. Finally, the matrix

question format asked respondents about the importance of the following attributes in

determining whether or not they like a song. These attributes included “familiarity”,

“popularity”, “fits my mood”, “artistic talent”, “lyrics”, and “friends’ preferences”, and were

classified as either “Not important”, “Somewhat important”, “Very important”, and

“Extremely important”. Again, these closed-‐ended questions ensured that respondents

measure their perspectives by the same standards, which was one of the primary reasons

for using the online survey approach.

The next page of the survey examined the respondent’s music listening

environment. Using the matrix question format, the respondents indicated how often they

listen to music in each of the following environments and activities, including “At home”,

“In the car”, “At work”, “By yourself”, “With a few friends”, “At a party”, “While studying”,

and “While sleeping”. Potential responses utilized the Thurstone scale, and included

“Never”, “Rarely”, “Sometimes”, “Often”, and “Always”. While these activities and locations

may have overlapped somewhat, each item was chosen for relevance and potential to


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influence music preferences. Perhaps the most pivotal of the entire survey, the next matrix

question asked respondents to rank their “Favorite”, “2nd Favorite”, and “3rd Favorite”

music services between iTunes, Pandora, and YouTube; respondents could also select a

fourth option, “Never use it”. These music services were selected because they are widely

used, legal alternatives to music piracy and because I wanted to understand how they are

reshaping the music industry from individual users’ perspectives.

The following three pages contained contingency questions depending on whether

respondents use the services iTunes, Pandora, and YouTube. Using similarly structured

matrix questions, these pages sought to ascertain the perceived impact of each service on

users’ music preferences and spending habits based on the Likert scale. For example,

respondents were asked to indicate various levels of agreement/disagreement with the

statements “As a result of using Pandora,” “I listen to music more often”, “I listen to a wider

range of genres”, “I listen to more music within the genres I like”, “I share music with my

friends more”, “I buy more music”, “I buy different music than I would have otherwise”, “I

have bought concert tickets that I wouldn’t have otherwise”, and finally “music plays a

bigger role in my life.” Because these questions directly apply to the hypothesis of this

study, they did not contain negative answers or answers that might bias results, and there

were several different units or elements intending to measure the same concept. The final

question on each page asked respondents to choose their favorite feature of each service,

choosing between “customizability/personalization”, “its interface”, “its wide selection of

music”, “playlisting and song recommendation”, and “Other: Please Specify”. The Likert

scale was chosen both for its speed and appropriateness in this case, and the use of similar


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questions on the pages for all three music technologies will ensure a common standard of

measurement and enable a closer comparison of their relative impacts on users.

Additional measures taken to ensure accuracy of questionnaire responses include

carefully ordering the questions in ascending order of difficulty, eliminating double-‐

barreled questions, providing questionnaire instructions, and pretesting the questionnaire

on a number of classmates. Wherever possible, questions with similar potential responses

were grouped as matrix questions to quicken response times and maintain a higher

response rate. Furthermore, to improve the relevance of the questionnaire, the questions

that may not apply to all respondents have been formatted as contingency questions.

Coding and Data Analysis

In order to analyze the results of the online questionnaire, I downloaded the CSV file

of raw data for 138 respondents from www.rationalsurvey.com and imported it into SPSS

Statistics, which I purchased through Stanford Software Licensing. Preparing the survey

data for analysis involved several steps, the first of which was removing the incomplete and

age-‐inappropriate cases. After deleting the few cases of respondents who were no longer in

college or hadn’t completed the survey, I ended up with 125 total respondents. Next, I

defined each of the variable properties by classifying them as either ordinal, nominal, or

scale. I then used a number of coding techniques to enable tests of correlation, assigning

numeric values to all textual responses. For example, “Never” = 1, “Rarely” = 2,

“Sometimes” = 3, and so on. Next, I assigned corresponding labels to the numeric values to

facilitate my interpretation of statistical procedures. Due to the relatively large number of


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questions, 73 in total, the various strategies used to assign numeric values will be discussed

in tandem with the results and analysis of each variable.

Respondents

Due to the financial and temporal constraints of this study, the online survey was

distributed to a convenient sample. Of the 125 college students who completed the survey,

66 (53%) were male and 59 were female. Since I was targeting the college student

demographic, respondents had an average age of 21.43 with a standard deviation of 4.1. In

response to the third question of hometown, 63 respondents (50%) indicated they were

from Arizona, 40 of which were from Phoenix. Another 26 respondents (21%) hail from

various cities in California, and the remaining subjects’ hometowns included 18 states and

4 locations outside the United States. Although the survey’s findings may have a slightly

southwest/west coast bias, I found this geographical spread acceptable given the study’s

constraints.

Results

While the online questionnaire consisted of five sections, analysis of results was

divided into three sections: music preference and contributing factors, choice of music

technology and contributing factors, and impacts of each music technology on preference

and spending. Each of the three sections contains several different variables that measure

similar ideas to reinforce findings. Since nearly all variables were coded into numeric

values and most of these were ordinal, a simple function in SPSS created a spreadsheet of

all correlations between variables and designated those of significance at the .05 and the


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.01 levels. Because the survey was distributed to a convenient sample, statistically

significant correlations cannot be generalized to larger populations. However, these

findings may be used to speculate about how college students consume music online and

how technology influences their preferences. Due to the length and comprehensiveness of

the survey, the three results sections include only the most significant and/or surprising

results.

Music Preference and Determining Factors

Song Preference

Perhaps the most direct question addressing the factors affecting music preference,

question 16 asked respondents to indicate the importance of six attributes in determining

whether or not they like a particular song. In the interest of saving respondents’ time, I

selected attributes that were highly likely candidates of influence. Based on my experience

with music and friends’ preferences, I expected popularity and friends’ preferences to rank

the highest. After all, it seems like the two most persuasive reasons to check out a new song

are that friends love it or everybody else does. I also speculated that lyrics would receive

polarized ratings of importance, and that “fitting the mood” would rank as more important

than most of the other attributes. In hindsight, the attribute “artistic talent” should have

either been reworded as “musicianship” or juxtaposed with “producer’s talent”; as it

stands, it seems hard to believe many respondents would indicate that they don’t care if the

artist is talented.


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The results for these six attributes were fairly surprising, and have tremendous

implications for the improvement of music recommendation. First, my predictions about

popularity and friends’ preferences were almost completely wrong; respondents rated

both lowest, between “somewhat important” and “not important”, on average.

Furthermore, average responses for familiarity were positioned just above “somewhat

important”, illustrating users’ comfort with music exploration. Next, lyrics ranked third

with an average response just above “very important”, in contrast with my expectation that

some respondents preferring instrumental music or songs by Justin Bieber would consider

lyrics of minimal importance. Interestingly enough, importance of lyrics was negatively

correlated with preferences for electronic music and positively correlated with R&B/Soul,

both of which make sense. Although I guessed “fitting the mood” and “artistic talent” would

rank fairly high, I didn’t expect them to rank highest overall with an average response

between “very important” and “extremely important.” While these findings don’t prescribe

an ideal way to incorporate each attribute into song recommendations, they do suggest

that the traditional mechanisms of music discovery are far less effective than new

recommendation technologies that utilize this information.


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Figure 2

Admittedly these findings are self-‐reported and it’s entirely possible that people

simply don’t want to recognize how much a song’s popularity or their friends’ tastes

influences their own preference. To approach the question of how popularity impacts song

preference from a different angle, I examined the frequency of responses for question 15

that addressed feelings toward top 40 music (Figure 3). The average response was halfway

between “Some” and “About Half”, suggesting that the previous findings were correct.

Furthermore, a significant portion of respondents, reaching almost 20% of the sample,

state that they either don’t pay attention to top 40 charts or they like none of the songs on

them. This implies that although many users’ music tastes are still influenced by top 40

music charts, these indicators of popularity may be losing the power they once held over

AM/FM radio audiences.

0

0.5

1

1.5

2

2.5

3

3.5

4

Familiarity Popularity Fits Mood Artistic Talent

Lyrics Friends' Preferences

Not Im

portant Very Im

portant

Determining Factors of Song Preference - Mean Response


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Figure 3

Genre Preference

The survey’s first and simplest measure of respondents’ music preferences entailed

rating fourteen distinct music genres. The rating scale included “dislike” = -‐1, “neutral” = 0,

“like” = 1, and “love” = 2. Rock, Alternative, and Hip Hop/Rap scored the highest on

average among the 125 respondents, with Latin and World ranking lowest (Figure 4).

Additionally, the ratings for Hip Hop/Rap and Country were the most polarized, yielding

standard deviations over 1.

0 5 10 15 20 25 30 35 40

I don't pay attention to top 40s

None

Some

About half

Most

All or almost all

Preference for Top 40 Music


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Figure 4

After seeing how respondents ranked each genre independently, I wanted to know

how genres clustered together based on these ratings. Using multidimensional scaling in

SPSS, I determined the coordinates for each genre to create a Euclidean distance model that

provides a visualization of the similarities between genres based on the respondents’

rankings (Figure 5). Though the interpretation of the axes is essentially meaningless, this

graph is simply a way to visualize perceived similarities between genres according to

respondents. For the most part, these groupings of genres make sense when considering

musical similarities, probable listening environment, and several other characteristics.

0

0.5

1

1.5

2

Genre Preferences - Mean


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Figure 5

Next, respondents indicated their favorite genre, choosing from these fourteen and “Other”

(Table 1). Consistent with Christenson and Peterson’s findings, the “Other” category ranked

fourth largest with 14 respondents, verifying the importance of accounting for

“metagenres” and subgenres in music classification and recommendation. However, for the

purposes of this analysis, metagenres and subgenres were ignored to facilitate quick and

accurate responses.

Favorite Genre Respondents Favorite Genre Respondents

Rock 29 Pop 6

Hip Hop/Rap 18 Reggae 5

Alternative 16 Dance 4

Other 14 Classical 2

Country 13 Jazz 2

Latin

Dance Pop

Electronic

Jazz

Rock

Alternative

Hip Hop/Rap R&B/Soul

Classical

World

Country

Reggae Vocal

Derived Stimulus ConSiguration - Euclidean Distance Model


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R&B/Soul 7 Vocal 2

Electronic 6 Latin 1 Table 1

While the average genre ratings and favorite genre for all respondents are informative and

fairly interesting, these metrics’ true function is to provide a basis for correlations between

subgroups of the college student sample. These subgroups are drawn from four categories

of variables: music preferences, user demographics, musical experience, and listening

environment.

Factors in Music Preference

Correlations Between Genre Preferences

By having almost daily conversations about music preferences with friends and

strangers for at least ten years, I developed a few theories regarding relationships between

genres. I got the sense that people who listened to at least one niche genre tended to like

almost all others as well, and people who preferred popular music had much narrower

tastes for genres. While portions of the Euclidean distance model conveyed similar

information, the best way to test this claim was through bivariate correlations. Using the

spreadsheet of Spearman correlations, I calculated the number of significant correlations

between genres and found two groups of genres separating from one another. I created one

table using the genres with many positive, significant correlations (Table 2) and another

for those with fewer positive correlations and more negative correlations with other

genres (Table 3).


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Table 2

Significant Correlations With Other Genres Mostly Niche Genres

Positive Negative Reggae 7 0 Vocal 7 0 Latin 7 0 World 7 1 Classical 7 1 Jazz 7 1

Alternative 5 0 R&B/Soul 5 0

Table 3

Significant Correlations With Other Genres Mostly Popular Genres Positive Negative

Hip Hop/Rap 6 2 Dance 4 1

Electronic 3 0 Pop 3 1 Rock 2 1

Country 2 1

These tables provide strong evidence supporting my claim that users who like one

niche genre are likely to enjoy many more. Not only does it show that niche genres are

positively correlated with many others (Table 2), the more popular genres have twice as

many negative correlations (Table 3). Hip hop/rap was the one genre positioned in

between the distinct groups but was included in the second table because it had the most

negative correlations. These findings seem to confirm my hypothesis that fans of niche

genres have wider preferences and fans of popular genres have narrower preferences.

Demographics and Genre Preferences


25

I predicted a few demographical variables from the first page of the survey would

correlate with genre preferences so I examined their Spearman correlations. As I expected

based on Christenson and Peterson’s study and my own experience, gender was negatively

correlated with preferences for Dance, Pop, and Country. Since I assigned the values

“Female” = 1 and “Male” = 2, this means that females are more likely to enjoy these three

genres and males are less likely. While this isn’t an especially groundbreaking conclusion, it

both makes sense and matches up with Christenson and Peterson’s findings, adding a

degree of confidence to other correlations with genre preferences.

I found another fairly predictable correlation between age and preference for jazz

and classical music. Since the correlations were both significant and positive, we can

conclude these two genres are more appealing to older respondents. While this isn’t

incredibly surprising, it’s interesting to consider that the standard deviation of

respondents’ age was only 4.1. This means that just a few years of age separates the fans of

classical and jazz from those who enjoy these genres much less. It’s difficult to determine

whether this is caused by a generational difference or perhaps a difference in maturity

levels, but simply knowing the correlation could improve song recommendations

significantly.

On the other hand, I found an unexpected correlation between competence using

computers and preferences for electronic music at the .01 level. Put simply, the more

experience respondents had with computers, the more likely they were to like electronic

music. While this correlation makes sense because the creation of electronic music

requires digital signal processing, I was surprised that electronic music was both the only


26

genre correlated with computer skills and it was significant at the .01 level. Again, these

correlations at the .01 level can’t be generalized to the population, but instead indicate

particularly strong correlations between variables for the college students in the

convenient sample. This particular correlation between a genre preference and computer

skills, a characteristic seemingly unrelated to music, begs the question of how many other

personality-‐based characteristics correlate with music preference.

Although I was expecting a greater number of correlations between demographic

information and genre preferences, those that I found present convincing evidence for the

implementation of demographics in music recommendation technology. iTunes, Pandora

and YouTube already attain demographic information and incorporate it to varying

degrees when serving up recommendations. But the more personality-‐based information

these services can capture without annoying users, the more they can measure correlations

and target recommendations. Whether this implementation involves data mining from

public social media profiles or building extended social profiles within a music application,

it has potential to dramatically improve music recommendation. The key is to convince

users they are benefitting each time they build out their profile and use A/B testing to

ensure that recommendations improve.

Musical Experience and Genre Preferences

I expected the survey questions addressing musical experience to correlate strongly

with genre preferences. I based this hypothesis on two observations of my own experience

with music. First, the more time I spent listening to music, the more I got bored listening to

the same few genres and tended to explore unfamiliar genres. Second, playing guitar has


27

had a tremendous impact on my music preferences and listening habits, and I expected this

trend to hold true for other people regardless which instrument they play. The plethora of

studies on the effect of music education on preferences also motivated me to include these

measures in the study (LeBlanc). To my knowledge, the music recommendation

technologies of iTunes, Pandora, and YouTube don’t take users’ musical experience into

account and I felt this represented an opportunity for improvement. Although genres were

the only feasible metric of music preference for the purposes of this analysis, future studies

may address correlations between musical experience and song preferences.

Upon examining the spreadsheet of bivariate correlations generated in SPSS, I found

four variables of musical experience that correlated strongly with several genre

preferences. First, listening hours per week correlated positively with electronic and jazz

music at the .05 and .01 levels, respectively. Although correlations with other genres

weren’t statistically significant, all were positive except country music. This proves that

listening to music more often facilitates a wider range of preferences and correlates

strongest with electronic and jazz.

Next, I examined how musical education in both schools and private lessons

correlated with genre preferences. I expected the two metrics to have similar correlations

with genre preferences, and hypothesized that higher levels of music education would

correlate positively with preferences for niche genres. As it turned out, “musical education

in school” correlated positively with classical at the .01 level and with jazz and world at the

.05 level. On the other hand, “years of private music lessons” correlated positively with

preferences for classical, world, and rock, but negatively with country. While none of the


28

other genres had statistically significant correlations, I noticed a general trend of negative

correlations between musical education and preferences for popular genres like dance,

pop, hip hop/rap, and country. Additionally, I found statistically significant positive

correlations between years of experience playing an instrument and preferences for rock

and classical, with six more genres producing positive correlations that were above the .05

level. These findings generally confirmed my hypotheses and show that music

recommendations may be improved by accounting for users’ music experience, though a

more thorough study using song preference is necessary to substantiate these conclusions.

Listening Environment and Genre Preferences

The final category of variables I analyzed in conjunction with genre preferences was

respondents’ listening environment. I examined respondents’ views across eight distinct

listening environments according to the following coded indicators of how often they

listened to music in each: “Never” = 1, “Rarely” = 2, “Sometimes” = 3, “Often” = 4, and

“Always” =5. The average responses and their standard deviations are represented in

Figure 6.


29

Figure 6

Again I explored the spreadsheet from SPSS that highlighted significant correlations

between these eight metrics and genre preferences. In much the same way that

correlations between genres divided the genres into two distinct groups (Tables 2 and 3),

the variables for listening environment separated into three separate groups (Table 4). The

first group of listening environments included “In the Car”, “Studying”, and “Sleeping”, and

more frequent listening in these environments was correlated with higher ratings in

several genres, with no negative correlations. The next group consisted of “At Home”, “By

Yourself”, and “At Work”, and had one or fewer correlations with genre preferences. The

final group of environments was more social than the other two, and had an equal or

greater number of negative correlations than positive correlations.

3.83

4.73

3.07

4.07 3.63

4.36

3.27

1.89

0

1

2

3

4

5

6

At Home In the Car At Work By Yourself

With Friends

At a Party Studying Sleeping

Never Rarely Som

etimes Often Always

Frequency of Listening in Environments and Activities

+1 σ

Mean

-‐1 σ


30

Table 4

Significant Correlations With Genres Listening Environment

Positive Negative In the Car Rock, Hip Hop/Rap, R&B/Soul, Country 0 Studying Jazz, Latin, Classical, Reggae, Rock 0 1 Sleeping R&B/Soul, Latin, Classical 0 At Home Hip Hop/Rap 0 By Yourself Pop 0 2 At Work 0 0

With Friends Hip Hop/Rap Classical, World 3

At a Party Dance, Pop, Hip Hop/Rap Classical, World, Vocal

At first glance, the first and third groups of Table 4 might appear to be a DJ guide

indicating which genres should and shouldn’t be played in each environment. However,

these are only correlations between frequency of listening in eight environments and

ratings for genres; respondents were not asked directly which genres they listen to in each

environment. But since they follow such a logical pattern, it’s clear that listening

environment plays a pivotal role in determining which genres users listen to. At the very

least, these correlations provide evidence that music services using recommendation

technology should experiment with allowing users to adjust for different environments,

especially in lean-‐back music experiences like Pandora.

Choice of Music Technology and Determining Factors

Although forcing respondents to choose one favorite service may have made for

simpler analysis, I assumed most people use more than one of the three music services in

question. So, I asked respondents to rank the three of them in order of preference and


31

included the option “Never use it”. I then organized the results into a simple bar graph for

comparison (Figure 7).

Figure 7

The first conclusion these results present is that the majority of college students use

several music services, whether for different situations, genres, moods, or for other

undiscovered reasons. The three technologies I explored are some of the most popular, but

if Anderson’s long tail theory applies to music services as well as songs, the majority of

users also occasionally take advantage of other niche music sites. Responses were

relatively balanced between the three, with 50 survey respondents (40%) indicating

Pandora as their favorite. Another 39 college students chose YouTube as their favorite and

the remaining 36 respondents chose iTunes. YouTube performed the best overall, ranking

highest in both the 2nd Favorite and 3rd Favorite categories thanks to the fact only 6% of

respondents never use it. After determining the music apps’ relative rankings across the

entire sample, I explored trends among subgroups using the crosstabs function in SPSS as

well as bivariate correlations. Using this information, I speculated about causal

0

10

20

30

40

50

60

1st Favorite 2nd Favorite 3rd Favorite Never use it

Respondents

Music Technology Preference

iTunes Pandora YouTube


32

relationships between respondents’ preferred music service and all other collected

variables and set out to determine their relative influence.

Factors in Music Technology Preference

Favorite Feature

Arguably the most logical factor influencing the choice between iTunes, Pandora and

YouTube was respondents’ favorite feature, which I examined first. For each service, survey

questions asked respondents to choose their favorite feature from the following list:

“customizability/personalization”, “interface”, “wide selection of music”, “playlisting and

song recommendation”, and “Other: Please Specify”. Using these canned responses and the

option of free response, the three music applications could be easily compared while

capturing any features missing from the list. Results for favorite feature of the three music

services are visualized in Figure 8.


33

Figure 8

When inspecting Figure 8, it’s important to keep in mind that respondents chose

their single favorite feature, and those with fewer votes aren’t necessarily poor features.

YouTube’s clear favorite feature was its wide selection of music, earning 69 votes. This

result held true with my expectations; not only does YouTube offer the widest selection, it’s

also the only free on-‐demand service of the three. Had I anticipated Spotify’s entrance into

the US when I designed the survey, “wide selection of music” would have been an

interesting metric with which to compare Spotify and YouTube. Respondents’ favorite

feature of Pandora was understandably playlisting and song recommendation, though its

customizability/ personalization and wide selection of music also ranked high. The

simplicity of its interface may account for Pandora’s lower score on this metric, but this

also makes Pandora especially user-‐friendly, likely contributing to it earning the most votes

for favorite music service. Respondents’ votes for favorite feature of iTunes were fairly

0

10

20

30

40

50

60

70

80

Customizability / Personalization

Interface Wide Selection of Music

Playlisting and Song

Recommendation

Other

Respondents

Favorite Feature

iTunes

Pandora

YouTube


34

evenly distributed, with wide selection of music and interface ranking highest. Almost 20%

of respondents also used the free response option for iTunes, attesting to its variety of

useful features.

Based on these results, I came to three conclusions regarding the features of iTunes,

Pandora and YouTube. First, college students go to YouTube first to find any song that

exists. These findings are consistent with my own personal experience, and more

respondents chose wide selection as their favorite than the other four features combined.

Second, Pandora has the strongest song recommendation and personalization of the three

music apps under review. In an increasingly fast-‐paced world, users appreciate the easy,

personalized lean-‐back experience that Pandora offers for free. Third, iTunes is the most

robust and comprehensive music service of the three and its intuitive interface has set the

industry standard. Though it seems unlikely that any one service could outcompete the

others on all four features, iTunes appears to be the only one trying out of the three.

Besides favorite feature, the factors that I anticipated to have the greatest influence

on choice of music service fell into three categories: demographics, musical experience, and

music preferences. I also hypothesized that listening environment would influence the

choice between iTunes, Pandora and YouTube, but there were almost no significant

correlations between them. Additionally, the primary reason I inquired about listening

environment was to explore how it correlated with music preference, not music technology

preference.


35

Demographics and Music Technology Preference

First, I hypothesized that respondents’ age would influence their choice between

iTunes, Pandora and YouTube. Although respondents were college students with an

average age just over 21 and a standard deviation of only 4.1, there was enough variation

to distinguish between the preferences of younger and older respondents. My instincts told

me that YouTube would appeal to younger respondents because they were more social

media savvy, already familiar with YouTube from viral videos, and more intentional in

music selection. On the other hand, I guessed that as people get older they were more likely

to use Pandora to DJ in the background whether for familiar songs or exploring

personalized recommendations. Additionally, I expected older respondents to favor iTunes

for its functional interface, expansive media store, and library for organizing CD’s.

As it turned out, my intuitions were fairly accurate. The correlation between

respondents’ age and their preference for YouTube was positive and significant at the .01

level. Since lower values for music service indicated stronger preference, this meant that

younger ages correlated with stronger preference for YouTube. Next, preference for iTunes

was negatively correlated with respondents’ age, significant at the .05 level. In other words,

older respondents were more likely to rate iTunes as their favorite service. And while the

correlation between rating of Pandora and respondents’ age wasn’t statistically significant,

it was also negative. Even though all three music apps collect users’ age upon creation of

new accounts and have quite a few more data points than my survey, it’s unlikely they

track users’ relative preference for the other two options. And since most college students


36

use at least three music sites, it’s important to know which they prefer in order to speculate

and test why they do.

Next, I explored gender as a determining factor of favorite music service. Based on

conversations with friends and DJs from Stanford and Phoenix, I predicted that females

would gravitate toward YouTube slightly more than males, but that males would prefer

iTunes more than females would due to its emphasis on customization and playlisting

features. Last, I expected Pandora’s audience to be more balanced between genders. Using

the crosstab function in SPSS, I organized the results and made several pie charts for visual

comparison (Figure 5).

Figure 9

Simply looking at the two groups of respondents’ first favorite service, it’s clear that

iTunes was most popular among males and Pandora was the favorite for females (Figure

9). Thus my hypothesis regarding males was correct but I failed to accurately predict


37

female behavior, which wasn’t incredibly surprising. YouTube won both the second and

third favorite categories among both males and females, as a result of so few respondents

indicating they never use it. Even though the only statistically significant correlation was

between females and stronger preference for Pandora, surveying a larger sample might

confirm that iTunes tends to be the primary service for males and YouTube is more of a

secondary service for both genders.

The third factor in favorite music service that I explored was respondents’ school.

Though I focused my efforts on three schools, a number of students from other schools

found my survey either on Facebook or Twitter. For simplicity’s sake, I analyzed the three

schools I targeted (ASU, GCC, Stanford) and put all others into an “other” category for

analysis. I anticipated YouTube and Pandora would be most popular at GCC, which was the

largest group of respondents (71). I also predicted Stanford would favor iTunes, and ASU

would have a balanced distribution between the three services. As it turned out,

respondents’ schools had a substantial impact on their preference between iTunes,

Pandora and YouTube.


38

Figure 10

Partially due to ASU’s sample being too small, music technology preference varied

widely between the three targeted colleges. iTunes easily won the position of ASU’s

favorite service, while Stanford’s favorite service was split between iTunes and Pandora.

Furthermore, the correlation between preferences for iTunes and respondent’s school was

positive and statistically significant, meaning that students from Stanford and ASU were

more likely to prefer iTunes. One possible explanation for iTunes’ strong performance in

these two colleges is Apple’s strong brand presence on both campuses. Apple has large

offices near both Stanford and ASU, and tends to hire students from both schools. On the

other hand, GCC’s favorite service was balanced between Pandora and YouTube. As a

community college, GCC most likely has a greater percentage of students living on a budget,

which may explain why the two free services rank highest among them. Whether these


39

companies care about how they stack up among these colleges, these findings present

unique insights into the diffusion of music technology in different schools.

The fourth potential factor in music service preference that I analyzed was the

respondents’ perceived competence with computers. When asked to categorize their

competence using computers, the largest group of respondents indicated they were

“Average”, totaling 67. Another 38 indicated their computer competence was “Advanced”,

16 chose “Expert”, and 4 chose “Basic”. “None/Very Little” was also among the possible

categories of competence with computers, but unsurprisingly no respondents selected it.

Admittedly I didn’t expect respondents’ competence using computers to correlate with

their favorite music service as closely as other factors, but I did expect Pandora and

YouTube to perform strongest in the Basic and Average groups due to their simple

interfaces. I also predicted Advanced and Expert computer users to prefer the playlisting

and organizational features of iTunes. As it turned out, the relationship between

competence with computers and favorite music service wasn’t statistically significant.


40

Figure 11

In terms of respondents’ 1st Favorite music service, Pandora ranked highest among

Average and Advanced computer users, while YouTube performed best in the Basic and

Expert groups. But beyond that, it’s difficult to see any patterns between competence using

computers and favorite music service. The spreadsheet of Spearman correlations from

SPSS confirms this, showing no statistically significant correlations between the two

variables. However, stronger preferences for Pandora correlated with higher computer

skills, just above the .05 level. In this case, the concentration of responses in the middle two

groups suggests an inadequacy in the wording of the question for computer skills. Not only

is the wording general and vague, responses are self-‐reported and may not be accurate for

this variable.

Musical Experience and Music Technology Preference

I used two questions on the survey to address the music listening habits of

respondents. The first asked respondents how many hours per week they spent listening to


41

music with an open-‐ended response format. On average, respondents indicated they listen

to music 18.9 hours per week with a standard deviation of 16.5 and a range of 1 to 98

hours. For this variable, I decided to analyze only respondents’ favorite music service and

determine the average user’s listening hours per week for each service. Though all three

services have ways to track their users’ listening hours more accurately and on a larger

scale, my methods provide the college student perspective on the relative strengths and

weaknesses of each service in comparison to the other two. I predicted that the more

engaged listeners would favor iTunes and Pandora as their top choice, since these services

are geared more towards lean-‐back listening experiences. In my experience, YouTube is the

search engine of choice for recalling or discovering a particular song, or for sharing DJ

responsibilities with several friends. However, as it is an on-‐demand and therefore user-‐

controlled experience I would expect the respondents who primarily use YouTube to spend

less time listening to music.

The second listening habits question asked students to categorize how often they

listen to music online, choosing from “Never”, “Rarely”, “Sometimes”, “Often”, and “All the

Time”. Besides the difference that the first question addresses music listening in general

and the second addresses online music listening, the former is a scale variable and the

second is ordinal. I expected having both scale and ordinal variables would prove useful for

visualizing listening habits and provide a second measure to verify interesting differences.


42

Figure 12

Average listening hours per week turned out to be a much better metric than

frequency of online music listening, but both provide visual evidence of Pandora’s tendency

to attract heavy users. Users who selected Pandora as their favorite music service tended to

listen to music more often both in general and online. Furthermore, the correlation

between stronger preferences for Pandora and greater frequency of online listening was

statistically significant at the .05 level. This makes sense because Pandora requires very

little effort to start, continues playing similar music for several hours, and continues

indefinitely if the user gives occasional feedback. YouTube also offers somewhat similar

playlisting but this is a secondary feature and lesser known among respondents. iTunes


43

supports long listening sessions but requires more effort by the user to manage playlists,

not to mention buy or own the music in the first place.

The second portion of musical experience that I expected to influence music

technology preference was musical education. Three similar questions measured

respondents’ levels of musical education in school, years of private lessons, and years spent

playing an instrument. Though my only expectation regarding the first measure was that

Pandora would attract those with more music education in school, I was uncertain how

music technology preference would correlate with private music lessons. I expected

respondents who played an instrument to favor YouTube with its wide selection, tendency

to include lyrics in songs, and because so many musicians use it as a platform to showcase

their music. I also hypothesized that preference for Pandora would be stronger among

experienced musicians because of its personalized song recommendation and

implementation of professional musicians’ ratings. Results for music education in school,

private lessons, and years playing an instrument are displayed in Figures 13, 14 and 15,

respectively.

Figure 13

0 5 10 15 20 25 30 35

0 1 Level 2 Levels 3 Levels

Choice of Music Service vs. Music Education Levels

iTunes

Pandora

YouTube


44

To measure music education in schools, surveys asked respondents to select any

school levels during which they took at least one music class, choosing from “Elementary (K

– 8th)”, “High School”, and “College”. The number of checked boxes for each respondent

became their number of music education levels, totaling between 0 and 3. My hypothesis

about musicians tending to prefer YouTube was incorrect, possibly because of the

distinction between using YouTube to listen to music and using it to upload their own

material. The spreadsheet of bivariate correlations revealed that stronger preference for

iTunes was correlated with more years of music education, while stronger preference for

YouTube was correlated with fewer, both at the .05 level. A possible explanation for these

trends is that musicians prefer greater control over their music and enjoy making playlists.

More experienced musicians also might be more inclined to pay for their music because

they appreciate it more. Regardless of the reasons why, it’s clear that music experience

plays a substantial role in determining which music applications college students prefer.

Figure 14

Using the compare means function in SPSS, I calculated the averages and standard

deviations of respondents’ years of private music lessons in salutation to which music

3.69

2.02 2.55

-‐2

0

2

4

6

8

10


Years

Favorite Music Technology vs. Years of Private Music Lessons

+1 σ Mean -‐1 σ


45

service was their favorite. Respondents choosing iTunes as their favorite showed

significantly higher levels of experience with private music lessons, and this correlation

was statistically significant at the .05 level. Although a larger, random sample is required to

substantiate these findings, the similar results of music education in school add a degree of

confidence in this case.

Figure 15

I distinguished between years of private music lessons and years playing an

instrument because I personally have played guitar for over ten years and only took

lessons for about two of them. I wondered how my choice in music service might have been

affected by continuing lessons throughout the ten years, or if I hadn’t played an instrument

at all. Once again, iTunes was especially popular among the more experienced musicians,

while preference for YouTube was correlated with fewer years of playing an instrument,

both significant at the .05 level. I found this interesting because I used iTunes almost

exclusively throughout high school and started using Pandora and YouTube more when I

wanted to explore music that was similar or that my friends had shared with me.

2.22 1.76

1.54

0

1

2

3

4


Years

Favorite Music Technology vs. Years of Playing an Instrument

+1 σ Mean -‐1 σ


46

Music Preference and Music Technology Preference

The final group of variables I predicted would influence music technology

preference included genre preference, feeling toward popular music, and determining

factors of song preference. I was curious which preferred genres would correlate with each

music application and guessed that niche genres would be rated higher among heavy

iTunes users, and that preferences for YouTube and Pandora would correlate with more

popular genres. I based these predictions on my observations of friends using each service;

iTunes was the first choice for creating playlists of songs that had personal significance and

not necessarily musical similarity, as with Pandora. YouTube seemed to be the social music

discovery application of choice and my friends used Pandora to listen to stations based on

their favorite hit artists or songs. I used the compare means function in SPSS to determine

each genre’s mean rating depending on respondents’ favorite music service (Figure 16).


47

Figure 16

As it turned out, only a limited number of genres correlated significantly with music

service preference. Respondents who rated dance higher were less likely to choose iTunes

as their favorite, and more likely to choose YouTube. On the other hand, fans of rock were

much more likely to prefer iTunes and not YouTube. Interestingly, only one genre

(R&B/Soul) correlated with Pandora and it was barely significant at the .05 level.

Additionally, YouTube was correlated with stronger preference for hip hop/rap and Latin

music. Given that all three music services offer a wide range of genres, the lack of more

significant correlations is understandable.

The other two topics within music preference that I hypothesized would influence

choice of music technology turned out to be almost completely unrelated. The first was

feeling toward music from top 40 charts. Once again I expected Pandora and YouTube to

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

-‐1 = Dislike 0 = Neutral 1 = Like 2 = Love

Favorite Music Technology vs. Genre Rating (Mean)



48

correlate with positive feelings toward top 40 charts. While these predictions turned out to

be accurate, the correlations weren’t statistically significant. The second topic was factors

in song preference, measured by the six variables from Figure 2. The single variable that

was correlated with any of the music technologies was popularity, associated with weaker

preference for iTunes and significant at the .01 level. The collection of these three measures

of music preference did align with my expectations, but the lack of statistically significant

correlations suggests that a larger sample should be tested.

Music Technology Influencing Preference and Spending

Although the questions addressing how the three music services affect music

preferences and spending were placed at the end of the survey, I view them as the most

significant to the future of the music industry. The first statistical procedure I carried out to

analyze the results was a simple descriptive function in SPSS, which produced the mean

responses and the standard deviations for all 125 respondents. To facilitate visualization of

the results, responses were coded as follows: “strongly disagree” = 1, “disagree” = 2,

“neutral” = 3, “agree” = 4, and “strongly agree” = 5. Since the question format was identical

for the three services, comparing the results by service revealed interesting trends.


49

Figure 17

On average, respondents indicated that all three services make them listen to music

more often and listen to more music within genres they like. Pandora and YouTube users

also indicated they listen to a wider range of genres, share music with their friends more,

and that music plays a bigger role in their life. Although the other average responses don’t

rise above Neutral (= 3), their standard deviations show that many respondents agree that

they buy more music, buy different music, and even buy concert tickets as a result of using

iTunes, Pandora and YouTube. The differences between each music service’s ratings along

these metrics present interesting considerations regarding contributing factors.

Effects on Preference


50

More Listening

The first measure of impact on music preferences was the most basic; respondents

indicated whether they agreed or disagreed with the statement “I listen to music more” as a

result of using each music service. Pandora ranks the highest on this metric, with “strongly

agree” almost within one standard deviation. YouTube and iTunes then follow Pandora,

and all three services rank between “agree” and “strongly agree” with one standard

deviation above the mean.

Figure 18

This order may be explained by the relative ease of use of each music service. First

time Pandora users enter an artist, song, or genre to start listening to a station, and the

music automatically starts for returning users upon revisiting the site. Similarly, using

YouTube simply requires knowing the artist or song name and one more click starts the

music. While iTunes has plenty of the same tricks to speed up the time it takes to find a

song, artist or playlist, users may perceive iTunes as requiring more time and effort.

However, the discrepancy between services along this metric is likely influenced by a

3.21 3.76

3.47

0

1

2

3

4

5


Disagree Neutral Agree I listen to music more

+1 σ Mean -‐1 σ


51

number of other factors. Most importantly, these findings confirm that all three services

facilitate and increase music listening to a greater extent than other music technologies.

Wider Range of Genres

Next, respondents indicated their level of agreement with the statement “I listen to a

wider range of genres” as a result of using iTunes, Pandora, and YouTube. This metric is

significant because services that expand their users’ range of genres are shifting both

listening hours and revenue toward songs and artists that would have remained

undiscovered otherwise. Again, Pandora ranked highest with 68% of respondents

indicating either “agree” or “strongly agree.” YouTube was ranked second with an average

score just over “neutral” and iTunes scored a full point below Pandora with an average

score leaning toward “disagree”.

Figure 19

Pandora’s higher rank in this area isn’t particularly surprising given that the Music

Genome Project, its core technology, recommends new songs based on 400 attributes and

occasionally serves up songs from neighboring genres. Similarly, YouTube’s personalized

2.76

3.89

3.16

0

1

2

3

4

5


Disagree Neutral Agree

I listen to a wider range of genres

+1 σ Mean -‐1 σ


52

“Recommended Videos” and “Suggestions” both surface songs that may fit within other

genres than the original song. Furthermore, the sheer size of YouTube Music provides the

widest selection and its heavily used social functionality increases cross-‐genre exposure on

Facebook and elsewhere. While iTunes has almost as wide a music selection as YouTube,

songs on iTunes must be purchased to hear more than a preview and users must be slightly

more proactive to find unfamiliar music. The two most likely explanations for these

rankings are the differences in primary mechanisms for discovering new music and the

relative emphasis on musical “horizon widening” by iTunes, Pandora and YouTube.

Deeper Within Familiar Genres

The next measure of impact on music preferences flows logically from the previous

metric. Respondents chose their level of agreement with the statement “I listen to more

music within genres I like” as a result of using each music service. While expansion across

genres certainly benefits both the user and the industry, exposure to new music within

familiar genres has a stronger impact and is more likely to inspire the user to purchase. On

average, the surveyed college students indicated that all three services deepen their

familiarity with music genres they like. Pandora won its third category in a row with 73%

of respondents selecting “agree” or “strongly agree”. YouTube ranked second with a mean

of 3.53, followed closely by iTunes averaging just above “neutral” at 3.18.


53

Figure 20

Pandora’s Music Genome Project uses a combination of musicologists’ ratings and

user feedback to create personalized radio stations themed by artist, song, or genre. As a

result, this technology deepens users’ knowledge and appreciation of familiar genres by

definition, so it’s understandable Pandora ranks highest. Again, YouTube ranks second in

this measure, which is likely a byproduct of its personalized recommendations, similar

video suggestions bar, and social features. However, it’s surprising respondents didn’t rank

iTunes as high with its Genius playlisting, iTunes Essentials mixes, and purchase history-‐

based song recommendation. While it’s possible these features are lesser known compared

to YouTube’s prominent next videos, a more likely explanation is that respondents

associate iTunes with music they already own. In this way, these users are more likely to

explore and discover music on a free streaming platform and perhaps switch to iTunes to

buy tracks they particularly like. Regardless, it’s clear all three music technologies are

helping users find good music and artists find new fans.

3.18

3.99 3.53

0

1

2

3

4

5



I listen to more music within genres I like

+1 σ Mean -‐1 σ


54

More Sharing Music

Historically, music discovery has been a profoundly social experience and I wanted

to measure and compare college students’ opinions on the social features of iTunes,

Pandora and YouTube. For this metric, respondents indicated their level of agreement with

the statement “I share music with my friends more” as a result of using the three services.

YouTube ranked highest among respondents in this area, with over 62% of respondents

agreeing or strongly agreeing. Pandora had the second highest rating along the sharing

dimension, with an average score just above neutral and “agree” within one standard

deviation. On the other hand, the average respondent said they didn’t share more as a

result of using iTunes and only 26% indicated otherwise.

Figure 21

According to YouTube’s press page, Facebook users watch over 500 years of

YouTube videos everyday, and over 500 YouTube links are tweeted every minute. YouTube

Music makes up approximately 31% of all videos, and it’s likely a greater percentage of

shared videos are music related. Sharing music from YouTube is both easy and popular,

2.69 3.07

3.64

0

1

2

3

4

5



I share music with my friends more

+1 σ Mean -‐1 σ


55

especially for the college student demographic. Last summer Pandora underwent a

complete site redesign into HTML5 and introduced a number of social features including

the ability to follow friends’ music activity, view a personalized stream of music activity,

and share songs or stations through Pandora, Facebook, or Twitter. As respondents

showed, these features put Pandora ahead of iTunes on social functionality, but still well

behind YouTube. iTunes also introduced a social feature called Ping in late 2010 but it

seems to have been fairly ineffective, at least with college students. As these music

applications’ social integration becomes more intuitive and familiar to users, the process of

sharing music will continue to scale and improve both engagement and spending.

Effects on Spending

More Buying

The first measure of the three music technologies’ impact on spending was perhaps

the most important question of the survey, at least to the music industry. The proliferation

of illegal filesharing applications like Napster, Kazaa, and Limewire has caused upheaval

among record labels and artists, and for good reason. While there are important benefits to

free exchange and many artists are experimenting with free mixtapes and similar

promotions, most musicians struggle to make a living even without having to worry about

digital piracy. This portion of the survey was intended to measure the impacts of legal

music applications on individual users’ spending. Respondents indicated their level of

agreement with the statement “I buy more music” as a result of using each application.

Consistent with the industry-‐wide trend of declining sales, all three services’ averages

ranked between disagree and neutral, with iTunes ranking highest. Pandora followed


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iTunes with 17% of respondents buying more music and YouTube ranked third with only

15% in agreement.

Figure 22

As the only music service of three with a fully integrated music store, it’s

understandable iTunes ranks highest in this metric. A likely contributing factor, iTunes’

personalized “Genius” song recommendations are shown prominently in both the offline

application and the home page of the iTunes store. All songs, artists and albums within the

offline library link to the iTunes store to facilitate the music shopping process, and iTunes

also recently increased song preview time limits, presumably for the same purpose.

Despite these features, the average college-‐aged respondent doesn’t buy more music as a

result of using iTunes. While the wording of the question prevents us from knowing if users

are buying less, it’s reasonable to assume that the 30% of respondents who strongly

disagreed are buying less. Pandora and YouTube both feature links to purchase music in

iTunes and elsewhere, but the findings of this survey show that only a small percentage of

users actually click through to music stores.

2.61 2.46 2.38

0

1

2

3

4

5



I buy more music

+1 σ Mean -‐1 σ


57

Buying Different Music

Second only to buying more music, this metric has tremendous implications for the

music industry and directly addresses the primary inquiry of this study. In an effort to

explore the consequences of transitioning from AM/FM radio to digital music applications,

the survey asked respondents whether they bought different music as a result of using

iTunes, Pandora and YouTube. Although iTunes ranked highest on the buying more music

metric, Pandora ranked highest for buying different music, and YouTube slightly edged out

iTunes for second place. “Agree” fell within one standard deviation of Pandora’s average

response, with over 35% of respondents agreeing or strongly agreeing, compared to

YouTube’s 16% and iTunes’ 18%.

Figure 23

The most intriguing aspect of these findings is not that Pandora ranks higher than

iTunes, but that Pandora’s average response for the “buy different music” metric is higher

than its average response for “buy more music”. Though the majority of users don’t

perceive Pandora as increasing their music purchases, a significant portion believe Pandora

2.31 2.88

2.41

0

1

2

3

4

5



I buy different music

+1 σ Mean -‐1 σ


58

is redirecting their music spending. It’s also important to note that Pandora pays artists

both performance and composition royalties every time a song is played, regardless of

whether the user skips it. In this way, users indirectly support unfamiliar music simply by

using Pandora, whether they end up buying or not. As mentioned earlier, iTunes Genius

and YouTube’s recommended videos, social functionality, and links to iTunes all contribute

to the portion of respondents who say they are buying different music, though the majority

indicate otherwise.

Buying Concert Tickets

The third possible effect on spending that the survey measured was users buying

concert tickets that they otherwise wouldn’t have, as a result of new music technology.

Based on my preliminary study’s findings for a Communication course at Stanford, I didn’t

expect more than a handful of respondents to agree for any of the services. While this

metric did measure the lowest on average for all three, both Pandora and YouTube

performed much better than expected. Respondents ranked Pandora highest once again

with over 17% agreeing or strongly agreeing. YouTube’s agree and strongly agree groups

were even at 6% each, and iTunes had 5% indicating agree and none for strongly agree.


59

Figure 24

There are a number of possible explanations for Pandora and YouTube’s albeit slight

victory over iTunes in this measure. First, both services monetize through advertising on

pages with content that is either mostly or completely music-‐based. This makes both

services prime locations for concert advertising. Second, two of the primary motives for

attending concerts include discovering new music and sharing the experience with friends,

both of which Pandora and YouTube users enjoy online. iTunes’ lower ranking may be

explained by the fact it now sells live albums in the iTunes Store, reducing some users’ need

to attend in person.

Music is Bigger

The final measure of impact on spending was admittedly vague, asking respondents

to indicate their level of agreement with the statement “music plays a bigger role in my life”

as a result of using the three music apps. Although its implications are difficult to quantify,

it addresses the idea that new music technology brings users closer to their favorite songs

and artists, which benefits everyone in the industry. While Pandora ranked highest on

1.82 2.43 2.25

0

1

2

3

4

5



I buy more concert tickets

+1 σ Mean -‐1 σ


60

average, respondents had more polarized opinions of YouTube, bringing its upper standard

deviation above Pandora’s. iTunes scored an average just under neutral, with over 33%

agreeing or strongly agreeing.

Figure 25

Due to the abstract nature of the question and the closeness between the three

services, analyzing any causes for difference would be purely conjecture. However, this

particular metric suggests that at least half of college students are more engaged in music

as a result of using the three music apps in question. In constructing this portion of the

survey, the previous metrics followed a general order of increasing levels of engagement.

By placing this metric at the end, it’s possible I primed some respondents to select lower

levels of agreement than if I had placed at the beginning. One could argue that the first

metric, listening to music more, would qualify as music playing a bigger role in

respondents’ lives, despite the latter scoring lower overall.

2.96 3.35 3.30

0

1

2

3

4

5



Music plays a bigger role in my life

+1 σ Mean -‐1 σ


61

Discussion

My love for music and new recommendation technologies drove me to explore how

other college students navigate the unlimited choice of digital music. Growing up I noticed

how often people complained that the radio only played the same ten songs. But after a few

failed attempts at DJ-‐ing parties with family and friends, I understood that radio stations

could only take a limited number of risks in terms of unfamiliar or irrelevant music. I also

quickly learned that my parents and their friends didn’t enjoy hard rock the same way that

my garage band-‐mates and I did. Witnessing the fundamental power of music in my own

life, I became especially curious about how music preferences are formed and transformed.

Through this study, I was able to answer many of the questions I had pondered about

individuals’ tastes in music, and also came up with several new questions to explore in the

future.

In my analysis of the six factors impacting song preference, I was fairly surprised by

how low respondents’ ranked the importance of popularity and friends’ tastes. These two

factors arguably played the biggest roles in determining song preference prior to the

Internet, and now seem to be of secondary importance. The two most important factors in

song preference turned out to be “fitting the mood” and “artistic talent,” the former of

which may be served by emotion-‐based music recommendation, user-‐generated tags, or

Pandora’s themed radio stations. The much greater perceived importance of “artistic

talent” compared to “popularity” seems to represent a growing trend among younger

generations. Adolescents subscribing to this “hipster” attitude assume that popular songs

are rarely made by artistically talented artists, and also that the fewer people that have


62

heard of their preferred artists or songs, the better. Those with a strong desire for

unfamiliar music tend to spend hours searching various obscure music sites and blogs,

while others are content with the occasional unfamiliar song on Pandora or Genius

recommendation on iTunes. New music technologies that account for varying degrees of

the hipster mindset will serve college users better at the very least, and may also widen the

musical horizons of older generations accustomed to AM/FM radio and other traditional

music media.

The vast majority of my examination of music preferences focused on variables’

correlations with genre preferences. I found that users who liked at least one niche genre

were much more likely to enjoy several more genres, whereas more popular genres like hip

hop/rap, dance, and pop were associated with much narrower tastes in music. There were

also a number of significant correlations between genre preferences and demographic

information like age, gender, and computer skill level. While music technologies currently

track user demographics, this study’s findings suggest that their recommendation

algorithms should alter suggestions for different demographical groups. Additionally,

further research should explore correlations with artist and song preferences, and also

examine how personality-‐based questions relate to music tastes. For example, users who

play baseball may be especially likely to enjoy Jimmy Buffet’s music.

Both musical experience and listening environment also impacted respondents’

music preferences, revealing a need for further inquiry and possibly the incorporation of

this data in recommendation systems. Musical experience generally showed a predictable

trend of being positively correlated preference for niche genres, which almost undoubtedly


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applies to lesser known artists and songs as well. Despite the brevity of the survey’s section

on music listening environment, it uncovered interesting relationships between genre

preferences and the location, state, and activity of music technology users. Once again, the

width of topics that the survey addresses and non-‐random sampling prevent definitive

conclusions in these areas, warranting additional study.

I personally use at least seven different music sites and services on a weekly basis,

and I’ve always loved hearing how others find music online. In this study I selected the

most popular and purely legal music applications, narrowing down the list to my three

favorites for more focused analysis. Though Pandora turned out to be respondents’

favorite, most students indicated that they use all three services and probably use several

more. The most relevant question on the survey regarding the reasons to choose one

service over another asked respondents their favorite feature. Results showed that users

favor YouTube for its wide selection of music, Pandora for its song recommendation and

personalization, and iTunes for its interface and range of features. Although iTunes’

apparent balanced effort between features appealed to many respondents, Pandora and

YouTube’s domination of one or two core features seemed to elicit more passionate

responses from users.

Besides preference for core features, respondents’ demographics played an

important role in the determination of music technology preference. First, younger

respondents favored YouTube while older respondents favored iTunes. These correlations

may be due to differences between age groups’ size of music collections, budgets for music,

music preferences, or other causes. Next, females were significantly more likely to rank


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Pandora as their favorite, while males were slightly more inclined to favor iTunes. The

surveyed college students from Stanford and Arizona State University were significantly

more likely to prefer iTunes, while students from Glendale Community College tended to

prefer Pandora and YouTube. Stanford and ASU students’ preference for iTunes is likely a

result of Apple’s strong brand awareness on both campuses, while GCC students’

preference for Pandora and YouTube may be a result of these being free sources of new

music. Three out of the four demographic variables that I analyzed were significantly

correlated with preference for one or more music technologies.

Musical experience and musical education both influenced choice of music

technology as well, to varying degrees. Respondents who chose Pandora tended to listen to

music more often both in general and online. In terms of music education, respondents who

had taken more music classes in school or more private instrument lessons were more

likely to use iTunes and less likely to use YouTube. This was surprising because I expected

musicians to use YouTube to showcase their work, but it’s likely that experienced

musicians have a greater appreciation for music and are therefore more willing to pay for

it. Additionally, musically experienced individuals listen to a wider range of genres and

appear to prefer iTunes’ interface that provides greater control over their listening

experience. Keeping these trends in mind, music services like Pandora and YouTube might

test and implement new subscription-‐based features that acknowledge users’ musical

experience and education, whether this means tweaking recommendation algorithms or

using an alternative interface for improved control.


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While knowing the factors that influence college students’ choice of music and music

technology is valuable, determining the effect of these technologies on music preference

and spending was the primary goal of this research. I predicted that the shift from AM/FM

radio to digital music would reshape demand in the music industry to lessen the

domination of hit artists and open up opportunities for lesser-‐known artists. As one might

expect, the simple answer is that it’s complicated. All three new technologies that I studied

benefit the music industry by increasing users’ engagement and expanding their

preferences both within and across genres. Pandora was the clear winner in terms of

causing users to consume music more often, listen to a wider range of genres, listen to

more music within familiar genres, and buy different music. In this way, Pandora does the

most to support artists further down “the long tail”. On the other hand, YouTube appears to

enhance social music sharing better than Pandora or iTunes, and iTunes understandably

facilitates music purchase more than the other two. Future research may examine each

service’s effect in greater detail, and include other new music technologies as well.

Despite my best attempts to balance comprehensiveness and manageability, the

survey may have missed some factors that influence music tastes and choice of technology.

On the other hand, with a completion rate under 16%, the survey’s appearance or length

clearly dissuaded most potential respondents from completing it. After I finished designing

the survey, several new music applications like Spotify and 8tracks became prime

candidates for similar research, making me wish I could start over again. However, this

study’s multivariate approach contributed original and significant findings that

demonstrate the potential of new music technology to benefit college students, artists

along “the long tail,” and the music industry as a whole.


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References

Anderson, Chris. The Long Tail: Why the Future of Business is Selling Less of More. New York: Hyperion Books, 2006. Print.

Beer, David. "The Pop-‐Pickers Have Picked Decentralised Media: The Fall of Top of the Pops and the Rise of the Second Media Age." Sociological Research Online 11, no. 3 (2006).

Bourreau, Marc, Francois Moreau, and Michel Gensollen. "The Digitization of the Recorded Music Industry: Impact on Business Models and Scenarios of Evolution." SSRN eLibrary (2008).

Bryson, Bethany. ""Anything But Heavy Metal": Symbolic Exclusion and Musical Dislikes." American Sociological Review. 61.5 (1996): 884-‐899. Print. <http://www.jstor.org/stable/2096459>.

Christenson, P., & Peterson, J. (1988). Genre and gender in the structure of music preferences.Communication Research, 15(3), 282-‐301. Retrieved from http://http://crx.sagepub.com/content/15/3/282.full.pdf html

David, Shay, and Pinch, Trevor. "Six degrees of reputation: The use and abuse of online review and recommendation systems (originally published in March 2006)" First Monday [Online], (17 March 2011).

Gaffney, Michael, and Rafferty, Pauline. "Making the Long Tail visible: social networking sites and independent music discovery." Program: Electronic Library & Information Systems 43.4 (2009): 375-‐391. Academic Search Premier. EBSCO. Web. 17 Mar. 2011.

LeBlanc, A., Sims, W., Siivola, C., & Obert, M. (1996). Music style preferences of different age listeners. Journal of Research in Music Education, 44(1), 49-‐59. Retrieved from http://http://jrm.sagepub.com/content/44/1/49.full.pdf html

Lessig, Lawrence. Remix: Making Art and Commerce Thrive in the Hybrid Economy. New York: Penguin Pr, 2008. Print.

McGinn, Robert E. Science, Technology, and Society. Upper Saddle River, New Jersey: Prentice Hall, 1991. Print.

Monroe, Don. "Just For You." Communications of the ACM 52.8 (2009): 15-‐17. Business Source Complete. EBSCO. Web. 18 Mar. 2011.

Rentfrow, Peter J., Gosling, Samuel D.The do re mi's of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, Vol 84(6), Jun 2003, 1236-‐1256. doi: 10.1037/0022-‐3514.84.6.1236

Surowiecki, James. The Wisdom of Crowds. Anchor, 2005.


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Appendix

1. Full Survey


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2. Survey Recruitment Email

From: Andrew Penrose [email protected]

BCC: Stanford email lists, my parents’ students at Glendale Community College, ASU friends

Subject: Andrew's Brief Music Technology Survey (could change your life)

Hi Everyone, I'm writing my honors thesis on digital music technologies and I'm surveying college students to better understand the usage and effect of iTunes, Pandora, and YouTube. Please do me a favor and take 15 minutes to share your experience with online music and support my research. https://www.rationalsurvey.com/s/1524 Go ahead and forward this if you love good music and want to make it easier to find. :) I really appreciate it! Thanks, Andrew -- Andrew Penrose Science, Technology and Society, B.A. Honors Stanford Class of 2012 [email protected] | (602) 451-0150 | @apenrose3

the longer tails of itunes, pandora, and youtube

Entertainment & Humor

professor fred turner

euclidean distance model

social networking sites

glendale community college

private music lessons

private music lessons

surveyed college students

dont pay attention