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Factors influencing music piracyJames Popham aa Department of Sociology , University of Saskatchewan , 1019-9Campus Drive/Saskatoon, Saskatoon SK, S7N 5A5, CanadaPublished online: 03 May 2011.

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Criminal Justice StudiesVol. 24, No. 2, June 2011, 199–209

ISSN 1478-601X print/ISSN 1478-6028 online© 2011 Taylor & FrancisDOI: 10.1080/1478601X.2011.561648http://www.informaworld.com

Factors influencing music piracy

James Popham*

Department of Sociology, University of Saskatchewan, 1019-9 Campus Drive/Saskatoon, Saskatoon SK S7N 5A5, Canada

Taylor and FrancisGJUP_A_561648.sgm10.1080/1478601X.2011.561648Criminal Justice Studies1478-601X (print)/1478-6028 (online)Article2011Taylor & Francis2420000002011JamesPophamJames.popham@usask.ca

A number of studies have illustrated that age, sex, computer skills, access tobroadband Internet services, and number of devices owned by a respondent areeffective predictors of engagement in electronic music piracy. However, thesefindings have relied on data collected from undergraduate student samples. Thispaper reassesses factors of music piracy using a more representative sample of thegeneral population. Using a logistic regression model, the findings suggest thatmost of the variables considered in past research significantly increase the oddsconnected with public engagement in electronic music piracy.

Keywords: digital piracy; downloading; MP3; cybercrime; logistic regression;public engagement

Introduction

Past research has identified several factors that contribute to electronic music piracy(e.g., Gopal, Sanders, Bhattacharjee, Agrawal, & Wagner, 2004; Hinduja, 2001;Hinduja & Ingram, 2009; Morris & Higgins, 2009). The factors include the respon-dents’ computer skills, access to broadband Internet services, age, gender, and race.However, most studies were conducted within post-secondary institutions, usingsamples of undergraduate students (e.g., Higgins & Makin, 2004; Hinduja, 2006;Ingram & Hinduja, 2008). Since the student population is different from the generalpublic in demographic features, exposure to computer technology, and social activities(Ogan, Ozakca, & Groshek, 2008; Skinner & Fream, 1997), conclusions based onstudent samples may not apply to the general population (Flere & Lavric, 2008; Payne& Chappell, 2008).

This paper uses variables identified in past research to see if they predictparticipation in music piracy in the Canadian general population. The 2007 CanadianInternet Use Survey (CIUS) (Statistics Canada, 2006) provides data from a nationalrepresentative sample that can be used to conduct the test. There is some evidence toindicate that music piracy in Canada extends beyond the student population. TheOrganization for Economic Co-operation and Development (OECD) found that Cana-dians have the highest per-capita rate of music piracy among nations surveyed(Wunsch-Vincent & Vickery, 2005); furthermore, 40% of Canadian households usethe Internet to download music legitimately or otherwise, yet only 17% of Canadianhouseholds report having a student present (Statistics Canada, 2006).

Electronic music piracy refers to the illegitimate computer-aided copying, storage,and distribution of digitally compressed copyrighted audio tracks, commonly called

*Email: james.popham@usask.ca

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MP3s. Persons engaging in music piracy can make use of specialized computer soft-ware to copy music tracks from a compact disc (CD) and store them on their personalcomputer hard drive as MPEG-1 Audio Layer 3 (MP3)-formatted files. These filespose only a small demand on electronic storage resources, thus making it easy andconvenient to collect numerous MP3s, usually of pop music, and share them via theInternet (Okin, 2005). This technology has been accessible to the public since theearly 1990s; however, the enormity of its impact on music piracy was not known untilspecialized software called Napster emerged in 1999 (Knopper, 2009). The individualcould now easily collect and distribute MP3 files en masse, giving birth to peer-to-peer (P2P) file sharing between strangers around the world. Napster was first designedto promote music sharing on computer campuses leading the Recording IndustryAssociation of America (RIAA) to place blame for this phenomenon, and subsequentsales losses, on post-secondary campuses (Knopper, 2009; RIAA, 2010). Thisperspective has, perhaps, been excessively propagated to the public because of thelack of exploratory research noted above.

Literature review

It is estimated that the US economy loses $12.5 billion annually due to music piracy,and that as many as 70% of downloaded songs are illegal downloads that directlyreplace legitimate purchases of music (Siwek, 2007). The OECD states that more than24% of Canadian households download music illegitimately (Wunsch-Vincent &Vickery, 2005). The RIAA has taken exception to this form of deviance and initiateda number of litigious campaigns against music downloaders (Hinduja, 2006; Knopper,2009). The RIAA claims that the lion’s share of music piracy is attributable to post-secondary campuses throughout the world (2010). While occasional small claims havebeen filed against individuals, the most frequent and exorbitant lawsuits have beenleveled at post-secondary educational institutions (e.g., Lockwood & Oliver, 2008).

Research has provided some support for the RIAA’s claims. Skinner and Fream(1997) found a 35% participation rate in piracy among undergraduate students;Rumbough (2001) found that 60% of students admitted to using the Internet to illegallydownload music files. Recent studies have found even higher participation rates rang-ing from 75% to 85% of students (Ingram & Hinduja, 2008; Selwyn, 2008a). Thesefindings have prompted leading studies of electronic music piracy to focus on the post-secondary campus (e.g., Higgins & Makin, 2004; Hinduja, 2006; Ingram & Hinduja,2008; LaRose, Lai, Lange, Love, & Wu, 2006). Although this research has producedreplicable findings for campus-based samples, no effort has been made to test its viabil-ity for non-student populations.

While some authors have suggested that results from student-based samples forsociological studies can be applied more broadly (Mazerolle & Piquero, 1998, cited inHinduja, 2006; Nagin & Paternoster, 1993), recent arguments have stated that theseapproaches should be used cautiously at best (Bouffard, Bry, Smith, & Bry, 2008; Flere& Lavric, 2008; Payne & Chappell, 2008). The debate over undergraduate samples hascontinued for decades – Gordon, Slade, and Schmitt (1986) introduced the ‘science ofthe sophomore’ as a longstanding issue in studies of the science of human behaviorwith early concerns dating back to the 1940s (p. 191). Their study on the subject foundthat the social background of participants in a number of studies significantly impactedtheir responses to questionnaires, rather than factors identified in applied theory. Theauthors then suggested that phenomenological differences between the somewhat

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narrowed histories of students versus the wide range of experiences in the public callsthe ‘experimental approach’ of extracting data from convenience samples into question(Gordon et al., 1986). The experimental approach has more recently been explored byKam, Wilking, and Zechmeister, who discuss the use of students as a ‘reliance on anarrow data base’ (2007, p. 416), making external validity the Achilles’ heel of human-ities research (p. 435). While they maintain that in some instances student conveniencesamples can be effective, they suggest that this Achilles’ heel can be mitigated throughthe use of other relative conveniences – such as readily available sources at the library,or equally accessible university employees (Kam et al., 2007). The debate on general-ization from specific samples is of particular importance to this study. Electronic musicpiracy is not confined to the campus, and consequently the external validity of existingstudies – and their samples – must be tested before policy implications can beestablished (Knopper, 2009).

Findings have emerged from many of the above-mentioned studies that suggestelectronic music piracy is a result of factors not directly connected to post-secondarycampuses, such as the respondent’s level of skill when using a computer, access tobroadband Internet services, age, gender, and race (e.g., Gopal et al., 2004; Hinduja,2001; Hinduja & Ingram, 2009; Morris & Higgins, 2009; Skinner & Fream, 1997).Skinner and Fream (1997) found that respondents with specialized skills, such asaccessing unique file-sharing resources, were more likely to report a higher level ofInternet piracy. Additionally, Hinduja (2001) demonstrates that access to a broadbandInternet connection is a predictor of participation in software piracy. Using a bivariatecorrelation matrix, Hinduja (2001) illustrates that electronic piracy is significantlycorrelated with access to high-speed Internet access. Furthermore, his findingssuggested that computer ability, in this case experience using computer hardware tocreate CDs that contained copyrighted software, accounts for 14% of the variation inInternet piracy among students (Hinduja, 2001). Hinduja (2001) remains somewhatunsure of the applicability of these findings to broader populations, noting that ‘thisparticular crime has been predominantly studied among college-aged individuals in auniversity setting. As such, additional research examining other populations mightretrieve different results and would provide interesting material for comparative anal-yses’ (p. 379).

A more recent study using a logistic regression model found that ownership of aniPod or a similar portable music device increases the odds of a person participating inmusic piracy (Holt & Morris, 2009). Holt and Morris’ (2009) research developed amodel which indicated that among other factors, multiple-computer ownership, self-reported computer skill, and race as well as the above-mentioned iPod ownershipsignificantly increased respondents’ odds of engaging in music piracy. These factorsare not strictly limited to the post-secondary campus, and if they prove to be significantin determining piracy among a non-student sample they will provide insight into theapplicability of a predictive model generated from post-secondary student datasets.

Other studies have established a connection between factors not necessarily relatedto undergraduate students and music piracy. Hinduja and Ingram (2009) have developedan ordinary least squares regression model indicating that a combination of electroni-cally developed peer networks, gender, Internet skill, and access to high-speed Internetaccount for 26% of the variation in respondents’ level of music piracy. Research byHiggins and Makin (2004) has also contributed to this model; the authors found thatengagement in music piracy is negatively correlated with age and females, and that SESand participation in copyrighted video piracy positively correlate with music piracy.

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Some researchers have confirmed the relevance of certain demographic variables.Gopal et al. (2004) found that younger males are most likely to engage in piracy; Morris,Johnson, and Higgins (2009) reported that non-whites are more likely to participate.Selwyn (2008b) found that gender and computer skills strongly correlated with musicpiracy. An interesting element added by Selwyn (2008b) is his qualitative finding thatInternet pirates feel safe to act illegally because of the anonymity of the Internet.

A model to explain music piracy has emerged. Mitigating factors aside, pastresearch has illustrated that access to high-speed Internet services, technical skill in anInternet-based environment, ownership of multiple technologies including computersand iPods, gender, race, and age all significantly contribute to engagement in musicpiracy (Gopal et al., 2004; Hinduja, 2001; Hinduja & Ingram, 2009; Morris &Higgins, 2009; Skinner & Fream, 1997). These findings have been so common thatthey have often been used as control variables when producing theoretical tests.However, as noted, supporting research has relied on samples based on post-secondarystudents. The effectiveness of this model to explain public engagement in musicpiracy remains tenuous.

Data and methods

The 2007 Canadian Internet Use Survey (CIUS) was conducted in conjunction withthe 2007 Labour Force Survey (LFS) by Statistics Canada. CIUS participants werecontacted by Statistics Canada representatives using telephone data collection tech-niques between October and November of 2007. The LFS was designed with theintent of producing a representative sample of Canadians aged 15 years or older livingwithin Canada’s 10 provinces. Persons living in any of Canada’s territories or Indianreserves, employed full time in the military, or incarcerated in any institution were notincluded. Overall this sample is representative of 98% of the Canadian population(Statistics Canada, 2007).

The 2007 CIUS had 26,588 participants; however, for the purpose of this paperseveral selections were applied which reduced this number. Approximately 38%(n = 10,211) of the respondents did not respond to questions about downloadingmusic, either because they had not used the Internet within the last year, or becausethey use the Internet only in a professional setting. For the purpose of this research,respondents who indicated they were active students were not included, eliminating7% (n = 2041) of the sample. Furthermore, respondents who indicated that they hadused the Internet to both download music and purchase music were not includedbecause their responses suggested that they had downloaded music through legiti-mate means (i.e., via the Apple iTunes store or other specialized websites). Thisprocess eliminated another 4% (n = 895) of the responses. These processes left asample of 13,351 Canadians who were not enrolled as students and who had eithernot downloaded music or used the Internet to download music without purchasing itonline.

Logistic regression is used to analyze the dependent variable of whether a personhad used the Internet in the past year to download music illegally. This statisticalmethod is commonly used in criminological research because it can effectivelyaddress the often dichotomous outcome of dependent variables presenting relativelyeasy-to-interpret coefficients for a predicted outcome (Britt & Weisburd, 2010). Thetwo-step process in these calculations first involves developing an odds ratio ofaffirmative and negative indications in the binary dependent variable relative to each

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independent variable, and then using the natural logarithm of the odds ratio to developmodel coefficients of change for the variables. Simply put, logistic regression analysisindicates the rate at which the odds of responding affirmatively to a dependentvariable increase with the level of participation in an independent variable (Britt &Weisburd, 2010). For instance in this study logistic regression analysis will demon-strate how changes in variables such as computer expertise impact the odds thatparticipant will have used the Internet to obtain music without paying for it.

The logistic regression process develops a model coefficient for each variable andits impact on the dependent variable independently of other variables. Several inde-pendent variables were used, including age, sex, computer ability, having a high-speedInternet connection, and the number of Internet-connected devices that respondentshold. Although race was also identified as a factor predicting music piracy (Gopalet al., 2004), no relevant variable was available in this dataset.

The variables – sex and high-speed Internet connection – were both coded asdummy variables (1 = male; 1 = having a high-speed connection). Age was recordedinto three categories: (1) 16–34, (2) 35–54, and (3) 55 or older. No direct variable waspresent for the number of devices owned by respondents; however, an alternativevariable was derived by summing the number of devices that respondents indicatedthey use to connect to the Internet. This new variable was coded in descending orderwith 1 representing four devices and so forth.

Respondents’ adeptness with computers was measured based on their reportedyears of experience with the Internet. The amount of time one spends using the Internethas been illustrated as a viable factor for predicting one’s level of expertise (Martinez-Lopez, Luna, & Martinez, 2005). Much like the age variable, the amount of time spentusing the Internet was broken down into several descending order categories: five yearsor more of experience was represented as 1, two to four years as 2, and less than twoyears of experience as 3.

Unfortunately, no variable in this dataset addressed participants’ race, and so therewas no opportunity to explore this dimension. Nonetheless useful variables wereavailable for the majority of the aspects of the model outlined above.

Results

Table 1 illustrates the bivariate relationships between the independent variables iden-tified above and music downloading. All independent variables have significanteffects on the independent variable. Furthermore, the relationships appear to followthe predicted directions.

Age and downloading show a negative relationship; respondents aged 16–34reported a 56% rate of engagement, while those aged 55 or older reported only15.3%. The number of devices owned and engagement in piracy are positivelyrelated in that more than 60% of respondents accessing the Internet from four devicesreported downloading music as opposed to 29.4% of those who report only owningone device.

The strength of these relationships indicates the effectiveness of the modeloutlined above to predict engagement in music piracy. As age increases, the likelihoodof engagement decreases; sex plays a slight but significant role in downloading music;access to high-speed Internet at home has a more pronounced relationship; and thenumber of Internet-connected devices owned by the respondent clearly correlates withengagement in music downloading. The only variable that produces mixed results is

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computer ability; however, a positive relationship is present between the number ofyears of experience a person has with the Internet and their likelihood to report havingdownloaded music in the past year.

The logistic regression model further illustrates the predictive capacity of themusic piracy model outlined above. The results of this model are illustrated in Table2. With the exception of the measures of computer ability, each variable signifi-cantly influences the dependent variable; within the ability model, only the highestscoring variable proved to be significant. These findings confirm the viability of thismodel for predicting respondents’ engagement in downloading music using theInternet (Table 2).

The respondent’s age has proven to be a particularly strong predictor of download-ing music. When compared to the ‘elderly’ reference group, participants in the‘young’ group were six times more likely to download. The odds of respondents in the‘middle-aged’ group downloading music were also twice as high as the referencegroup. Access to high-speed Internet at home also proved to be a significant predictorof the dependent variable; respondents without access to this technology were only44% as likely to download as those who did have access. On the other hand, genderwas much less likely to predict music downloading, with males being only slightlymore likely to participate.

The variables gauging computer experience provide mixed results. Although thedependent variable is not significantly influenced when respondents have betweentwo and five years of experience, respondents with five or more years of experience

Table 1. Bivariate comparison of dependent and independent variables.

NumberPercent downloaded

music within last year Chi-square

Age 1405.68*16–34 (Young) 3432 56.535–54 (Middle-aged) 6359 29.455 and older (Elderly) 3560 15.3

Sex 25.99*Male 5948 34.9Female 7403 30.8

High-speed Internet at home 229.90*No 1922 17.4Yes 10996 32.4

Computer ability 118.56*5+ years of use 9369 35.52–4 years of use 2602 26.1Less than 2 years of use 1373 32.6

Number of devices 219.56*One 9755 29.4Two 2459 37.1Three 643 48.8Four 208 60.6

*Significance level < 0.01.

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are 1.2 times more likely than those with zero to two years to download. The numberof Internet-enabled devices used at home by the participant proves to be of greatersignificance; respondents who report four such devices are more than twice as likelyto download music as those who report one device, while participants with three andtwo are 1.5 and 1.1 times more likely, respectively.

The remaining statistics in Table 2 illustrate the ‘goodness of fit’ of this model.The significant -2LL score indicates a high likelihood that the observed results aresimilar to the predicted results for the dependent variable and suggests that this is agood model. This is further augmented by an insignificant score for the Hosmer andLemeshow test, suggesting that the model used above has not occurred by chance.Additionally the model chi-square score suggests that the independent variablessignificantly improve the ability of this model to predict a dependent variable whencompared to a model using only constants (Munro, 2001).

Table 2. Logistic regression showing odds ratio of downloading music based on independentvariables.

Independent variables b Odds ratio

AgeAge recode

16–34 (Young) 1.862* 6.43635–54 (Middle-aged) .775* 2.17155 and older (Elderly)**

SexMale .177* 1.193Female**

High-speed InternetNo –.819* .441Yes**

Ability recode5+ years of use .217* 1.2422–4 years of use .009 1.009Less than 2 years of use**

Number of devicesFour .827* 2.285Three .430* 1.537Two .111* 1.117One**

Constant −1.024 .359Number of cases 13,351–2 Log likelihood 14621.74*Chi-square (Hosmer and Lemeshow test) 8.73Model chi-square 1630.77*

*Significance < 0.05.**Reference variables.

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Discussion

These findings demonstrate the usability of undergraduate-based research to predictelectronic music piracy in non-student samples. The implication of these findings isthat the growing body of literature exploring music piracy, largely based on conve-nience samples, can be applied to the general public. These independent variables,including age, sex, access to high-speed Internet at home, computer ability, and thenumber of Internet capable devices, have been used throughout this literature asexplanatory variables (e.g., Hinduja & Ingram, 2009; Morris et al., 2009) and may beuseful for future intellectual copyright policies.

For example, respondents indicating that they access the Internet at home throughbroadband technologies were more than twice as likely as those who did not to havedownloaded music in the past year. Considering that more than 70% of CanadianInternet users access their services through high-speed technologies (StatisticsCanada, 2008), the importance of this finding is clear. As broadband technologiesbecome more widely available, policy will have to adapt to reflect the potential forincreased delinquent online actions (i.e. illegitimate copying and sharing of popularmedia) that will come along with this growth.

Because the primary purpose of this research is to challenge the use of conve-nience samples for general use, the major policy implication is also centered on thistopic. In particular this research has addressed Kam et al.’s (2007) notion of exter-nal validity being the Achilles’ heel to human sciences. As established, a number ofcommonalities exist between the results of student-based research and the resultsemerging from a public sample. This seems to indicate that the data emerging fromstudent-based convenience samples may translate to the broader populationspectrum.

It should not, however, be implied that criminological research into electronicdeviance should ‘stay the course’ with regard to studies in computer deviance. Asnoted above there exist very pronounced differences in behavior across different agegroups. For example, younger respondents aged 16–34 were six times more likely touse the Internet for downloading music than persons aged 55 or older. This disparityseems to illustrate the point that research using undergraduate samples, a group witha much skewed age demographic, will be challenged when attempting to address asilent majority created by age differentiation. Rather the policy suggestion profferedhere is that alternative data sources should be used in conjunction with conveniencesamples to augment the reliability of findings. By combining qualitative research withquantitative criminological inquiries, a researcher can both illustrate their findings andargue for their generalizability concurrently.

A second notion emerging from this study is that the association of electronicmusic piracy as primarily an on-campus phenomenon is now a moot point. More thanhalf (56.5%) of all participants aged 16–34 years admitted to having used the Internetto download music without paying for it. Given the most recent demographic statisticsof the nation, approximately nine million Canadians fit into this age category, repre-senting 27% of the total population. With a 56.5% participation rate, these findingsindicate that five million young people, or 15% of the population, actively use theInternet as a tool for procuring music illegitimately (Statistics Canada, 2010). Further-more, significant participation was recorded from respondents aged 35–54 (29.4%)and participants aged 55 or older (15.4%), indicating that persons far removed fromthe postsecondary campus are also sourcing illegitimate means of collecting popular

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music. In regard to policy, these findings suggest that future preventative actions fromregulating bodies such as RIAA or the Canadian Recording Industry Associationshould address a wider population instead of current marketing campaigns directed atyouth.

In light of these results two considerations should be made. First, as noted above,high-speed Internet technologies have become common place in Canada. However, thishas not always been the case. Findings from the 2000 Household Internet Use Surveyproduced by Statistics Canada suggested that only 22% of Canadian householdsaccessed the Internet through high-speed services (Statistics Canada, 2001). On the otherhand, high-speed local area networks have been widely available at post-secondaryinstitutions for a much longer period of time (Crook & Barrowcliff, 2001), providingstudents with high data transfer speeds long before they were widely used in the public.This suggests that the predictive model used above may only be effective with modernpublic samples, and that further studies may be needed to understand downloadingbehaviors in regions without widespread high-speed Internet services.

The second consideration is clarified by looking at the timeline of electronic musicpiracy. June of 2010 marks the 11th anniversary of the introduction of Napster soft-ware. This file-trading program established the Internet as a crucible for electronicpiracy and initiated a boom for downloading that has not yet subsided (Knopper,2009). The highest odds ratio outlined above suggests that participants aged 16–34were six times more likely to download music than the elderly reference group. Theserespondents would have been aged between 5 and 23 when Napster made large-scalefile sharing available to the public; the oldest members of this group would have beenuniversity student aged, while the youngest would have enjoyed a decade high-speedInternet technologies and widespread file-sharing. This may indicate that persons withlong-term access to broadband technologies and P2P software are more likely todownload music than those who do not; additional studies are required to furtherexplore this possibility.1

Further research is needed to support these results. As noted above the data usedfor this paper were derived from the 2007 CIUS, which had been developed toproduce demographics. The present research would be better served by a more directquestionnaire that addresses some of the shortcomings listed above, such as bettermeasures of computer ability or more specific questions about engagement in musicpiracy. Nonetheless, this paper has produced a preliminary step illuminating theviability of convenience-based samples to predict music downloading behaviors in thegeneral public.

Note1. Further analysis was conducted controlling for age, and also for computer experience, to

gauge whether or not responses from youth were influencing the logistic regression. Nosignificant changes to the results were noticed.

Notes on contributorJames Popham received his bachelor of arts with honours in criminal justice and public policyfrom the University of Guelph, Ontario, Canada, and his master of criminology from GriffithUniversity in Brisbane, Australia. He is currently pursuing a PhD in sociology at the Universityof Saskatchewan with a focus on criminology and justice.

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