The Longer Tails of iTunes, Pandora, and YouTube


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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.

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The Longer Tails of iTunes, Pandora, and YouTube

  1. 1.     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    
  2. 2. 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  
  3. 3. 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  
  4. 4. 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  
  5. 5. 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  
  6. 6. 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  
  7. 7. 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  
  8. 8. 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”   (   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  
  9. 9. 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  
  10. 10. 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  
  11. 11. 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  
  12. 12. 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  
  13. 13. 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  
  14. 14. 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  
  15. 15. 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  
  16. 16. 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  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  
  17. 17. 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  
  18. 18. 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  
  19. 19. 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  
  20. 20. 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  
  21. 21. 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  
  22. 22. 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  
  23. 23. 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  
  24. 24. 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  
  25. 25. 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  
  26. 26. 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  
  27. 27. 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  
  28. 28. 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  
  29. 29. 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