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 apenrose@stanford.edu.

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 1

The Longer Tails of iTunes, Pandora and YouTube Penrose 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) 2

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 3

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 4

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 5

The Longer Tails of iTunes, Pandora and YouTube Penrose 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, 6

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 7

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 8

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 9

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 10

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 “NoneVery 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 11

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 12

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 13

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 14

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 15

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 16

The Longer Tails of iTunes, Pandora and YouTube Penrose .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. 17

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 18

The Longer Tails of iTunes, Pandora and YouTube Penrose Determining Factors of Song Preference - Mean Response 4 Not Important Very Important 3.5 3 2.5 2 1.5 1 0.5 0 Familiarity Popularity Fits Mood Artistic Lyrics Friends Talent Preferences 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. 19

The Longer Tails of iTunes, Pandora and YouTube Penrose Preference for Top 40 Music All or almost all Most About half Some None I dont pay attention to top 40s 0 5 10 15 20 25 30 35 40 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. 20

The Longer Tails of iTunes, Pandora and YouTube Penrose Genre Preferences - Mean 2 1.5 1 0.5 0 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. 21

The Longer Tails of iTunes, Pandora and YouTube Penrose Derived Stimulus ConSiguration - Euclidean Distance Model Rock Alternative Jazz Classical Electronic Vocal R&B/Soul Hip Hop/Rap Reggae World Pop Dance Latin Country 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 22

The Longer Tails of iTunes, Pandora and YouTube Penrose R&B/Soul 7 Vocal 2 Electronic 6 Latin 1Table 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). 23

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 Mostly Popular Significant Correlations With Other Genres 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 24

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 25

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 26

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 27

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 28

The Longer Tails of iTunes, Pandora and YouTube Penrose Frequency of Listening in Environments and Activities 6 Never Rarely Sometimes Often Always 5 4.73 4.36 4 4.07 +1 σ 3.83 3.63 3.27 Mean 3 3.07 -‐1 σ 2 1.89 1 0 At Home In the Car At Work By With At a Party Studying Sleeping Yourself Friends 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. 29

The Longer Tails of iTunes, Pandora and YouTube Penrose Table 4 Significant Correlations With Genres Listening Environment Positive Negative In the Car Rock, Hip Hop/Rap, R&B/Soul, Country 0 1 Studying Jazz, Latin, Classical, Reggae, Rock 0 Sleeping R&B/Soul, Latin, Classical 0 At Home Hip Hop/Rap 0 2 By Yourself Pop 0 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 30

The Longer Tails of iTunes, Pandora and YouTube Penrose included the option “Never use it”. I then organized the results into a simple bar graph for comparison (Figure 7). Music Technology Preference 60 50 Respondents 40 iTunes 30 Pandora 20 YouTube 10 0 1st Favorite 2nd Favorite 3rd Favorite Never use it 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 31

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 32

The Longer Tails of iTunes, Pandora and YouTube Penrose Favorite Feature 80 70 iTunes 60 Pandora Respondents 50 YouTube 40 30 20 10 0 Customizability / Interface Wide Selection of Playlisting and Other Personalization Music Song Recommendation 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 33

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 34

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 35

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 36

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 37

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 38

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 39

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 40

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 41

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 42

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. Choice of Music Service vs. Music Education Levels 35 30 25 20 iTunes 15 Pandora 10 YouTube 5 0 0 1 Level 2 Levels 3 Levels Figure 13 43

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. Favorite Music Technology vs. Years of Private Music Lessons 10 8 6 Years 4 +1 σ 3.69 2 2.02 2.55 Mean 0 -‐1 σ -‐2 iTunes Pandora YouTube 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 44

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. Favorite Music Technology vs. Years of Playing an Instrument 4 3 +1 σ Years 2.22 2 1.76 1.54 Mean 1 -‐1 σ 0 iTunes Pandora YouTube 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. 45

The Longer Tails of iTunes, Pandora and YouTube Penrose 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). 46

The Longer Tails of iTunes, Pandora and YouTube Penrose Favorite Music Technology vs. Genre Rating (Mean) 1.6 1.4 -‐1 = Dislike 0 = Neutral 1 = Like 2 = Love 1.2 iTunes 1 Pandora YouTube 0.8 0.6 0.4 0.2 0 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 47

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 48

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 49

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. I listen to music more Disagree Neutral Agree 5 4 3.76 3.47 3.21 3 +1 σ 2 Mean 1 -‐1 σ 0 iTunes Pandora YouTube 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 50

The Longer Tails of iTunes, Pandora and YouTube Penrose 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”. I listen to a wider range of genres Disagree Neutral Agree 5 4 3.89 3 3.16 2.76 +1 σ 2 Mean 1 -‐1 σ 0 iTunes Pandora YouTube 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 51

The Longer Tails of iTunes, Pandora and YouTube Penrose “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. 52

The Longer Tails of iTunes, Pandora and YouTube Penrose I listen to more music within genres I like 5 Disagree Neutral Agree 4 3.99 3.53 3 3.18 +1 σ 2 Mean -‐1 σ 1 0 iTunes Pandora YouTube 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. 53

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. I share music with my friends more 5 Disagree Neutral Agree 4 3.64 3 3.07 2.69 +1 σ 2 Mean -‐1 σ 1 0 iTunes Pandora YouTube 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, 54

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 55

The Longer Tails of iTunes, Pandora and YouTube Penrose iTunes with 17% of respondents buying more music and YouTube ranked third with only 15% in agreement. I buy more music Disagree Neutral Agree 5 4 3 2.61 +1 σ 2.46 2.38 2 Mean 1 -‐1 σ 0 iTunes Pandora YouTube 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. 56

The Longer Tails of iTunes, Pandora and YouTube Penrose 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%. I buy different music 5 Disagree Neutral Agree 4 3 2.88 +1 σ 2.31 2.41 2 Mean -‐1 σ 1 0 iTunes Pandora YouTube 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 57

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 58

The Longer Tails of iTunes, Pandora and YouTube Penrose Disagree Neutral Agree I buy more concert tickets 5 4 3 +1 σ 2.43 2.25 2 1.82 Mean 1 -‐1 σ 0 iTunes Pandora YouTube 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 59

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. Music plays a bigger role in my life 5 Disagree Neutral Agree 4 3.35 3.30 3 2.96 +1 σ 2 Mean -‐1 σ 1 0 iTunes Pandora YouTube 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. 60

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 61

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 62

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 63

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 64

The Longer Tails of iTunes, Pandora and YouTube Penrose 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. 65

The Longer Tails of iTunes, Pandora and YouTube Penrose 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 mis 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. 66

The Longer Tails of iTunes, Pandora and YouTube Penrose Appendix 1. Full Survey 67

The Longer Tails of iTunes, Pandora and YouTube Penrose 68

The Longer Tails of iTunes, Pandora and YouTube Penrose 2. Survey Recruitment Email From: Andrew Penrose apenrose@stanford.eduBCC: Stanford email lists, my parents’ students at Glendale Community College, ASU friendsSubject: Andrews Brief Music Technology Survey (could change your life)Hi Everyone,Im writing my honors thesis on digital music technologies and Im surveying college students to better understandthe usage and effect of iTunes, Pandora, and YouTube. Please do me a favor and take 15 minutes to share yourexperience with online music and support my research.https://www.rationalsurvey.com/s/1524Go ahead and forward this if you love good music and want to make it easier to find. :)I really appreciate it!Thanks,Andrew--Andrew PenroseScience, Technology and Society, B.A. HonorsStanford Class of 2012apenrose@stanford.edu | (602) 451-0150 | @apenrose3 75

The Longer Tails of iTunes, Pandora, and YouTube

by Andrew Penrose on Jul 09, 2012

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