a behavioral model of digital music piracy
TRANSCRIPT
A Behavioral Model of Digital Music Piracy
Ram D. Gopal Dept. of Operations & Information Management
School of Business University of Connecticut
Storrs, CT 06269 Email: [email protected]
G. Lawrence Sanders* 310A Jacobs Management Center
State University of New York at Buffalo Buffalo, NY 14260
Email: [email protected]
Sudip Bhattacharjee Dept. of Operations & Information Management
School of Business University of Connecticut
Storrs, CT 06269 Email: [email protected]
Manish Agrawal 310A Jacobs Management Center
State University of New York at Buffalo Buffalo, NY 14260
Email: [email protected]
Suzanne C. Wagner Niagara University
Dept. of Computer Information Sciences Niagara University, NY 14109-2019
Email: [email protected]
*: Corresponding Author
Revised January 2002
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A Behavioral Model of Digital Music Piracy
Abstract
The increasing pervasiveness of the internet, broadband connections and the emergence of digital
compression technologies have dramatically changed the face of digital music piracy. Digitally
compressed music files are essentially a perfect public economic good, and illegal copying of these
files has increasingly become rampant. This paper presents a study on the behavioral dynamics which
impact the piracy of digital audio files, and provides a contrast with software piracy. Our results
indicate that the general ethical model of software piracy is also broadly applicable to audio piracy.
However, significant enough differences with software underscore the unique dynamics of audio
piracy. Practical implications that can help the recording industry to effectively combat piracy, and
future research directions are highlighted.
Keywords: Digital music, Economics, Piracy, Ethics, Intellectual Property, Culture, Structural
Equation Modeling.
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A Behavioral Model of Digital Music Piracy
1. Introduction
Digital piracy is the illegal act of copying digital goods – software, digital documents, digital
audio (including music and voice) and digital video – for any reason other than backup, without
explicit permission from and compensation to the copyright holder (SPA 1997b). Digital media falls
under the purview of intellectual property and illegal duplication is prohibited by the U.S. and
international copyright laws and treaties (SPA 1997a). Despite this legal protection, digital piracy is
practiced in most countries around the globe (Antonoff 1987; SPA 1996). For instance, the software
industry is estimated to have incurred global revenue losses worth $11.4 billion in 19981. Contrasting
this with the worldwide revenues of business-based PC applications of $17.2 billion, highlights the
significant negative impact of piracy on the software industry.
Audio piracy, the illegal act of copying digital sound without explicit permission from and
compensation to the copyright holder, has recently exploded (IFPI 2000). Incentives to indulge in such
behavior are influenced by economic, technological and ethical considerations. Key technological
factors include the growing pervasiveness of the Internet, rapid adoption of broadband technology,
write-able CD2 technology, and the emergence of better compression technology3. This technological
advancement has many interesting consequences.
1 SPA's Report on Global Software Piracy(1998) http://www.spa.org/piracy/98report.htm. In January of 1999 the SPA merged with the IIA to form the Software & Information Industry Association (SIIA). The IIA represented companies involved in creating and distributing print in digital formats. 2 In the paper, reference to CD includes all recording media of high sonic quality. 3 MP3 (Mpeg 1 Audio Layer 3), a well-known audio compression technology, uses a compression algorithm based on a complicated psycho-acoustic model to create CD quality music at a fraction (about 10%) of the file size of the original song.
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• CDs can be created that contain over 160 compressed digital music files that can play for over 14
hours on a personal computer.
• An compressed music file (e.g. encoded in MP3) can be easily transmitted over the Internet.
• Digital music can be downloaded from the Internet into a portable music player. These players can
store several hours of digital-quality music and are smaller than a personal CD player.
These recent technological changes have transformed, what was until recently a mostly
domestic problem for individual countries, into an effective and effortless cross-border and trans-
continental music piracy. Much of the audio piracy activity is via the illegal copying of compact discs
and the downloading of audio files via the Internet. According to IFPI, a music watchdog body, the
piracy of digital audio has spread exponentially in the past three years. The number of infringing music
files available on the internet has increased twenty five fold in just three years, with 3 million
downloads of music a day. The global music piracy market was estimated to be 1.9 billion units in
1999 with an estimated value of $4.1 billion4.
Economic incentives to pirate digital audio include the high costs of purchasing legitimate
copies of audio CDs. If piracy behavior is modeled as a utility maximizing behavior where individuals
choose between illegal behavior that yields a positive consumer surplus, but carries the risk of
punishment, and legal behavior that carries lower consumer surplus but no punishment, higher music
purchasing cost would increase the payoff from piracy, ceteris paribus. Such an increase in the payoff
would naturally increase the likelihood for piracy, leading to greater illegal behavior (Ehrlich 1973). In
the domain of software piracy, such behavior has indeed been found, and increasing software prices
are generally correlated with increased piracy behavior (Cheng 1997, Gopal and Sanders 1997).
Recently, Gopal and Sanders (2000) have reported on a significant price and income effect related to
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software piracy rates.
The response of the recording industry to combat the piracy phenomenon has primarily been
two-pronged. The main emphasis has been to adopt legal measures against online sites that facilitate
widespread audio piracy. Simultaneously, they have realized the economic potential of offering online
music services and are working on developing technological solutions that enable the viable provision
of such services while protecting the copyrights of the legitimate owners.
One of the best known entities in digital audio file sharing is Napster, Inc. This free file sharing
service was started in May 1999 to allow users to search a centralized database and then download or
listen to music files stored on other users’ computers. Users could register for this service and
download or listen to music that they did not own in any other form. In December 1999, the Recording
Industry Association of America (RIAA) sued Napster in federal court in San Francisco alleging
copyright infringements (Clark 1999). In May 2000, the court ruled that Napster violated the Digital
Millennium Copyright Act.
As users of Napster’s original service began to dwindle, Napster began moving toward
legitimacy by negotiating distribution deals with record labels to launch an online music-subscription
service (Boston 2000). It also began using software from Relatable LLC to create the equivalent of
digital fingerprints of individual recordings, special files that can be used to identify and block
recordings from being exchanged.
However, this is not likely to end online music piracy. Other file-swapping systems are now
expected to grow in popularity, including the Gnutella file-sharing system and many sorts of "instant
messaging" approaches (Gomes 2001). Unlike Napster, these systems do not use a central database and
4 IFPI’s Music Piracy Report 2000. 1999 IFPI, http://www.ifpi.org.
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recording companies would quite likely have to sue individual customers, a prospect they have tried to
avoid (Ahlberg 2000, Bravin 2000, Clark 2000).
Many industry experts remain critical about the long-term effectiveness of focusing on Internet
“piracy facilitators” (Garber 1996; Hardie et al. 1999; Jerry 1987; Mason 1990). The concern
commonly expressed is that the “genie is now out of the bottle” and that simply shutting down such
services will have limited effect. As bandwidth continues to increase, and compression technologies
improve, users can continue to easily pirate songs (for example, emailing compressed songs to other
users) even if centralized servers are shut down. At its core, the overall piracy is a result of decisions
that individuals consciously make (Banerjee et al. 1998). The importance of ethics in modeling audio
piracy stems from efforts to study the related field of software piracy. The decision to pirate or not to
pirate an audio item, which is an intellectual property, is influenced by individual ethical conduct. An
understanding of the behavioral, especially ethical, dynamics that drive individuals to pirate music, and
more importantly, identification of the factors that can steer individuals towards purchasing legal
music can potentially help devise effective strategies to combat the exploding problem of music piracy
(Brady and Wheeler 1996; Cheng et al. 1997; Conner and Rumelt 1991; Glass and Wood 1996;
Harrington 1996; Loch and Conger 1996; Thong and Yap 1998). This is the central focus of our paper.
1.1. Related Research
Research on digital piracy is in its infancy. The significant focus in the literature to date has
been on software piracy5 (Conner and Rumelt 1991; Eining and Christensen 1991; Glass and Wood
5 A key reason is that, to date, the software industry has had the largest revenue losses due to digital piracy. Digital audio piracy is a relatively recent phenomenon.
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1996; Gopal and Sanders 1997; Jerry 1987; Mason 1990; Solomon and O'Brien 1991). The enormous
impact of software piracy on the software industry has spurred research on the behavioral and
economic understandings of software piracy activity. Studies have reported that females pirate less,
older individuals (as opposed to younger college students) pirate less, and that individuals with an
ethical predisposition towards legal justice (a primarily western notion; less important in the moral
makeup of the eastern cultures) tend to pirate less (Gopal and Sanders 1998). A key economic finding
related to software piracy is that deterrent controls result in higher profits to digital publishers and
higher levels of the welfare function than preventive controls (Blumstein et al. 1978; Gopal and
Sanders 1997).
Deterrent controls refer to the use of legal sanctions to check crime and include government-to-
government negotiations, educational campaigns, and legal activity related to expanding domestic
copyright laws and seeking to enforce those laws. These controls do not directly influence the cost or
effort of piracy. Rather, piracy is dissuaded by the perceived threat of such sanctions. Deterrent
controls, sometimes called back-end controls, are achieved through educational, legal and media
campaigns and are extensively used in software piracy. Their use in audio piracy has been relatively
limited as the companies have been reluctant to prosecute individual users for fear of annoying their
own customers.
Preventive controls attempt to decrease piracy by forcing the copier to expend resources in the
pursuit of piracy, and include software and hardware schemes to prevent the actual copying of the
software. It may also include innovative pricing mechanisms to make legal purchases more attractive,
or appeals to users to make ethical decisions. Examples of such technological controls include
software encryption and digital fingerprinting. They are becoming increasingly important in audio
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piracy as companies plan to develop online legal music services. For example, as described earlier,
Napster has begun using digital fingerprinting technology to identify the sound patterns in copyrighted
sound recordings and prevent such files from being shared. Encryption is another technique likely to
be used to prevent illegal duplication. Recently, Gopal and Sanders (2000) have reported on a
significant price and income effect related to software piracy rates. In a multi-national study of
software piracy, they found that countries with low per capita income had higher instances of software
piracy, ceteris paribus. Piracy was also positively affected by the price charged for the software
product. Hence price and income are potential economic and demographic determinants of music
piracy. The music industry is also appealing to consumers through artists who are losing revenue from
their intellectual properties as a result of online music piracy. The expectation is that it would lead
consumers to more ethical conduct and lower audio piracy.
Digital music shares a number of characteristics with software. Like software, it is expensive to
produce the first copy (high fixed costs) of music, and the cost to reproduce an additional copy is close
to zero. It also has the properties of a public good in that sharing with others does not reduce the
consumption utility. However, several factors underscore some key differences: (1) value degradation:
due to the utilization of compression technology, digital audio copies are inferior to the original; (2)
price differential: music, sold in a CD format, typically costs significantly less than a standard
software package, (3) support: unlike software the use of a digital audio file does not need any support
from the creator, (4) size: a digital audio file is significantly smaller in size than a software package,
and (5) volume: there are significantly more audio files than software packages.
A recent study has examined the impact of economic factors on audio piracy (Bhattacharjee et.
al. 2001). Their results suggest that an income effect, similar to software piracy, is present only for
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“unknown” songs. This suggests that individuals with lower incomes are likely to pirate rather than
purchase and sample “new” music, based on current prices. Absence of the income effect for known
songs suggests that the decision to purchase music containing favorite songs is not significantly
influenced by disposable income. The results also suggest a significant price effect on music piracy.
The economic rationale is that as the price increases, the net value from obtaining an illegal copy
increases, and hence the negative impact of price on piracy. The willingness to purchase is also
influenced by the availability of music and the connection bandwidth. These factors were found to
increase the price sensitivity of the music piracy. The willingness to pay was found to be higher for
‘known’ songs that users attribute a higher value to than for unknown songs of questionable value.
A number of studies have also focused on the importance of ethics on software piracy. The
underlying contention is that the decision to copy or not copy intellectual property is influenced by
ethical mores. Gopal and Sanders (1997, 1998) report evidence of a significant effect of ethics on the
individual behavioral mechanics of engaging in software piracy. Thong and Yap (1998) studied
softlifting using ethical decision making theories adapted from the marketing literature. They conclude
that efforts to encourage ethical behavior should include training in ethical analysis and an
enforcement of organizational code of ethics.
The purpose of this study is to examine the role of the ethical constructs known to be important
determinants of software piracy by individuals. Digital music exhibits different characteristics than
software, hence we will also examine the influence of deterrent strategies, demographic variables such
as age and gender, and music genre on digital music piracy. We begin with a model for music piracy
that is based on existing research on software piracy and evaluate the role of the different constructs in
the piracy of music. The results are expected to guide future efforts to check music piracy, primarily
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through a better understanding of individual ethical behavior.
2. Behavioral Model of Audio Piracy
The focus of this paper is to understand the behavioral dynamics of digital audio piracy
behavior. As noted above, researchers have proposed a variety of variables to explain an individual's
propensity and rationale for digital piracy, for example age, gender, attitudes, and ethical propensity.
Figure 1 presents the general model of ethical behavior developed by Gopal and Sanders (1998). Their
model was, in part, derived from the descriptive model of marketing ethics developed by Hunt and
Vitell (1986), the concept of ethical predisposition set forth by Brady and Wheeler (1996), and the
ethical decision-making framework developed by Raghunathan and Saftner (1995). Figure 2 presents
the model of ethical relationships related to digital piracy that will be examined in this study. The
primary research question to be answered is whether the music piracy model detailed in Figure 2 is
valid.
Research Hypothesis: A general model of ethical behavior applies to digital audio piracy.
An important feature of Figure 2 is that it also attempts to capture the economic benefits of
using downloaded songs (compressed in the popular MP3 and other formats). There are numerous
online sites containing compressed versions of legal songs that can be downloaded. They provide users
with the opportunity to sample and purchase different music genre and ultimately burn custom CDs.
These compressible formats facilitate the arrangement of songs on custom CDs where the total utility
of the CD generally exceeds the utility of any commercially available CD. This economic element is
measured by the “Money Saved Using MP3” construct in the research model.
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Methodology
A set of questionnaires was administered to 133 undergraduate students, most in their third
year of school, majoring in business. Subjects were assured complete anonymity. Although there is no
clear consensus on the optimal sample size for research involving structural equation models, a sample
size of between 100 and 200 for each group appears satisfactory (Bentler and Chou 1987; Fassinger
1987; Hair et al. 1995).The average age for the sample was 23 years. There were 61 females and 72
males in the sample. Additional statistics for the sample can be found in Table 1.
Club Size (Piracy Level): Music items exhibit the classic characteristics of a public good, where the
consumption utility of a consumer does not decrease when the music item is shared with other
individuals. This leads to the concept of a piracy club, where like-minded individuals associate
together to share and benefit from pirated music. The club purchases a legal copy of a music item at
market price and all club members make personal copies. The incentives for the members to form a
group include a taste for association and cost reductions from sharing fixed costs (Sandler and
Tschirhart 1980). The members of the group optimize benefits of cost savings from group expansion,
with the associated disadvantages of crowding, congestion and increased probability of detection.
Clubs do not require a formal membership process and may form informally when an individual
obliges an associate with a copy of the music with the implicit or explicit understanding of reciprocity.
Since it is the behavioral intention to pirate that leads to club formation, the Club Size is used as a
proxy to measure the behavioral intention to pirate music. Such a formulation is consistent with prior
research (Gopal and Sanders 1997, 1998).
Three items were used to operationalize the club size construct and they are shown in Table 2.
They describe hypothetical scenarios describing an individual making illegal copies for himself (or
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herself) at home, for a friend or family member and for some colleagues. The sum of these responses is
the club size. A higher scale value for the club size indicates greater intention to pirate . Cronbach's
coefficient alpha for the three club items is 0.88, indicating that the scale is fairly stable and consistent.
For additional details on the club measure see Gopal and Sanders (1997; 1998).
Ethical Index: The ethical index is a measure of individual ethical propensity, which is another
measure of the behavioral intention of individuals to pirate. The five items for this scale were adapted
from an instrument developed by Wood et al. (1988), and further refined by Gopal and Sanders (1997,
1998), to determine the ethical profile of respondents and is used here to capture behavioral intentions.
This is intended to measure the core beliefs of a respondent. The ethical index is computed by
summing the responses to five hypothetical situations listed in Table 3. A higher scale value indicates
higher ethical values. The Cronbach's coefficient alpha value for the five item scale is .79, indicating
that this scale is reasonably stable and reliable.
Justice--An Ethical Predisposition Dimension: Gopal and Sanders (1998) draw upon an extensive
review of the associated IS literature and the philosophy literature and suggest that an individual’s
ethical intentions are influenced by his or her expectations for the consequences of actions, based on
the consequentialism theories of ethical behavior. These theories suggest that individuals should
identify the consequences of their actions and behaviors and evaluate the goodness or badness of such
consequences. One way of making such a judgment is the principle of utility, which states that an
action is right if it tends to produce the greatest good for the greatest number of people. Such ethical
evaluation is likely to influence individual ethical behavior.
A measurement index for such evaluation used in prior research is an individual’s belief in the
justice system and the rule of law (Gopal and Sanders 1998). Four items, shown in table 4, were used
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to operationalize the Justice construct, a latent variable, as an ethical predisposition towards laws and
the justice system (Kant 1949; Rawls 1971). The items developed by Gopal and Sanders (1998) were
adapted from a variety of quotes and popular sayings and subjected to psychometric analysis. The
empirical results for the Justice construct are inline with the results reported by Gopal and Sanders
(1998), having strong factor loadings of .75, .94, .70, and .84. It should be noted that this construct
measures an individual’s ethical intentions prior to an action, which signifies an apparent deterrent
effect.
Money Saved Using MP3: One item was used to measure this variable: "How much money do you
save per year because you listen to MP3 songs?" The number of users indicating that they saved
money via downloading songs (compressed in the popular MP3 and other formats) was 56. Of the 56
individuals, the average amount of money saved was $249 with a standard deviation of $253 and a
range of $20 to $1,000.
3. Structural Equation Modeling
The structural equation model (Figure 3) tests the following null hypothesis:
H0: The model of behavioral determinants of music piracy is plausible in the population.
A significant chi-square value would indicate that the null hypothesis should be rejected
because the model does not fit the data and the model is not possible in the population ( Bollen 1989,
Fassinger 1987, Hair et al. 1995, Loehlin 1992). A low value of p would indicate that we cannot reject
the null hypothesis. The chi-square statistic obtained for this structural model was 32.46 with 24
degrees of freedom and a probability value (p) of 0.12. Hence the null hypothesis should not be
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rejected as the probability level of the chi-square statistic (p = 0.12). We therefore conclude that the
research model in Figure 2 is a viable representation of the relationships for behavioral determinants of
music piracy.
There is no one agreed goodness of fit measure for structural equation models (Chin and
Newsted 1995; Chin and Todd 1995). Various goodness of fit measures are used to compare the
estimated population covariance based on the structural equation model with the sample covariance
matrix that is calculated from the sample data. The following results present several goodness-of-fit
indices for this model and illustrate how they compare to the recommended values for the indices
when using maximum likelihood estimation of model parameters (Bentler and Chou 1987; Hu and
Bentler, 1999 ).
Goodness-of-fit Measure Observed Value Recommended
NNFI (TLI): Non-normed fit index .99 > = .95
IFI (BL89): Incremental Fit Index 1.00 > = .95
CFI: Comparative Fit Index 1.00 > = .95
RMSEA: Root mean squared error of approximation 0.05 < = .06
To the extent that the underlying assumptions hold, we can say that overall the structural
equation model provides a good fit for the data. The findings are, in general, similar to previous
research on software piracy as the paths are in the hypothesized directions.
The squared multiple correlation coefficients, which are similar to the coefficient of
determination values or R2 in regression analysis, are moderate. The squared multiple correlation
coefficient for the Ethical Index is 0.11 and for Club Size 0.28 (Figure 3).
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The path coefficients in Figure 3 are standardized partial regression coefficients. The strongest
relationship is between the Ethical Index and the Club Size. The path value (-.34) from the Ethical
Index to Club Size means that individuals from this particular population who are one standard
deviation above the mean for the Ethical Index will be -.34 standard deviations below the mean for the
Club Size, ceteris paribus. In other words ethical individuals will be less likely to form groups (the
Club Size is smaller) to share pirated digital audio files6. The path value (0.18) from Justice to Ethical
Index suggests that individuals from this particular population who are one standard deviation above
the mean for Justice will be 0.18 standard deviations above the mean for the Ethical Index, ceteris
paribus. Hence, higher levels of Justice are related to higher levels of the Ethical Index. Justice has a
very modest affect on Club Size, as a one-unit increase in the standard deviation of Justice is
associated with a -.07 increase in Club Size. The path (-.17) from Age to Club Size signifies that older
individuals will participate less in pirating digital audio files, ceteris paribus. Gender also has a modest
effect on the propensity to pirate, as the path coefficient from Gender to Club Size is 0.02.
The amount of money saved by downloading music files is a moderately strong predictor of
Club Size. The path value of .33 suggests that the greater the perceives amount of money saved, the
larger the value of Club Size.
To test the additional effect of income, another important demographic variable, on the club
size, the structural equation model was rerun with the income parameter included; however our
analysis showed that income did not influence the club size.. In a related research study investigating
the effect of income, income was found to have a negative effect – only for unknown songs
(Bhattacharjee et. al. 2001). In that study, income had no significant effect on an individual’s
6 Note that higher values for the Ethical Index imply being more ethical and higher values for the Club Size imply being less likely to form a pirating club and to engage in pirating MP3 files.
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inclination to buy known music items. This suggests that individuals with lower incomes are likely to
pirate rather than purchase and sample “new” music, based on current prices. Absence of the income
effect for known songs suggests that the decision to purchase music containing favorite songs is not
significantly influenced by disposable income.
The overall model fit indices, the squared multiple correlation coefficients for the constructs in
the model and the path coefficients lend support to the viability of the research model presented in
Figure 3.
As a final note, researchers must be careful in making comparisons with other studies, even
when using similar measurement scales, because of the decidedly contextual nature of behavioral
research. However, additional insight into the differences between pirating software and audio files
may be obtained by comparing this study with the study for software piracy. The squared multiple
correlation coefficients for this sample involving audio piracy are not as strong as for the US software
piracy sample (Club Size = .67 and Ethical Index = .63) reported by reported by Gopal and Sanders
(1998). They are in fact much more in line with the Indian sample in terms of the path coefficients and
the squared multiple correlation coefficients (Club Size = .12 and Ethical Index = .13).
We also investigated whether there was a deterrent effect in the form of knowledge about the
legal ramifications of pirating digital audio. The formal model tested was:
Club size = f (deterrence information)
We followed the experimental methodology of Gopal and Sanders (1997), where they found a
moderate deterrent effect on the formation of a software club. An additional 120 subjects were given
the original questionnaire detailed earlier; but they were also presented up-front with the following
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true news story from a widely distributed university newspaper.
Web Pirate: Copying Downloads For Friends? Get Out Your Checkbook The cost of college life just keeps on climbing. Just ask U. of Oregon senior Jeffrey Levy, who pleaded guilty to federal charges of distributing thousands of copyrighted songs, movie clips and software programs through his campus computer connection on August 20. Levy could now face up to three years behind bars and, get this, $250,000 in fines. That‘s right, $250,000. And you thought student loans were bad. At one point, Levy was passing the equivalent of about 250 full-length MP3 songs over the school network every hour. University officials noticed the hefty load of data going through Levy’s site and contacted law enforcement officials. It seems that Levy, a public policy management major, hadn’t been keeping up with current Congressional policies on copyright infringement. The No Electronic Theft (NET) Act, passed by Congress in 1997 under heavy pressure from the music and software industries, makes distribution of copyrighted material illegal even when there’s no profit involved. So, even though Levy wasn’t charging any money for access to his site, he’s going to have to pay up big-time. And surprisingly, students aren’t rushing out to support the web pirate. “If it was my program or music that someone was giving out for free, I’d want some type of retribution,” says Mitch Hochhauser, a sophomore at Syracuse U. “But for a college student who wasn’t making any money, jail time is too much. A large fine would leave any student hurting for a long time.” Ouch!
By David Konopka, Syracuse U. From: The National College U. Magazine, November 1999, p. 9.
In essence, the original 133 subjects did not receive the deterrence information and a separate
group of 120 students received the deterrence information. A regression run on the club size model did
not reveal a statistically significant t-value for the deterrence information coefficient. This suggests
that deterrent policies, which had a significant influence on software piracy, do not have similar effect
on digital music piracy. Some possible reasons for this observed difference between software piracy
and music piracy may be speculated. One possibility is that the respondents who were provided with
the story were not considering the kind of flagrant facilitation of piracy depicted in the true news story.
Another reason may be that individuals closely associate a software product with the organization that
produces it – hence they are aware of the legal ‘muscle’ of the organization. In the case of music,
consumers closely relate the product with the artist(s) producing it, rather than the music publisher or
producer. This disassociation with the music publisher may lead to a reduced appreciation of the full
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legal ramifications. This issue needs to be investigated further to devise an effective deterrent strategy
to check music piracy.
T-tests were also performed to determine if audio pirating activity was related to the music
genre. The individuals who listened to Hip Hop/Rap and Electronic music had a greater propensity to
pirate online digital music, but they did not exhibit a statistically different Ethical Index from the rest
of the sample. The implication is that individuals who listen to Hip Hop/Rap will tend to form digital
audio piracy clubs, but their ethical index is not markedly different from the other individuals in the
sample.
4. Discussion and Conclusions
A critical issue in digital audio piracy is the development of a behavioral model for digital
piracy activity. If music publishers have insight into the behavioral dynamics of audio pirates it may
lead to more effective educational and legal campaigns to educate users about copyright laws and
inspire attitudinal changes about appropriate copying behavior. Based on previous research results and
the results of this study, the model presented in Figure 3 provides a reasonable explanation for the
behavioral and ethical determinants of audio piracy activity. The enormous level of monetary
resources at stake warrants further investigation into other determinants of digital and, more
specifically, audio piracy behavior.
The first observation from the research is that the scales developed in prior research on
software piracy are reliable in the context of music piracy. The items in the scales for club size, ethical
index and justice have very stable factor loadings, indicating that these scales may be used for future
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research into individual determinants of music piracy.
The results indicate that age has a moderate influence on piracy. The popularity of piracy
among the respondents who preferred hip-hop/ rap music, suggests that demographic variables are
significant in the context of music piracy. The strong path coefficient from ethical index to club size
indicates that one possible means to reduce music piracy would be the use of awareness campaigns. A
greater awareness of the implications of piracy is likely to reduce actual piracy behavior. Measures
could include advertising campaigns and educational initiatives. Since these measures exhibit the
properties of public goods (efforts by one company can help all the other players), appropriate policy
initiatives (consortium formation, public intervention etc.) may be necessary for implementation.
However, this result is moderated by the matched samples test where the population of respondents
who were informed of the consequences of piracy did not behave differently with respect to club size
from the control group. It is possible that the intervention needs to be sustained over a longer period of
time before it is effective and that the type of campaign used to inform the public is important. The
weak relationship between the justice construct and club size indicates that strategies for public
awareness campaigns need to be examined carefully. Deterrent strategies used in anti-software piracy
campaigns often focus on legal issues and the potential for jail sentences and fines. This type of
campaign may not work to combat digital music piracy. Perhaps an appeal to altruism and support for
the arts would work to diminish digital music piracy.
Acknowledging the reality of piracy of music on the Internet, the music industry is taking
tentative steps to modify their existing business models to incorporate peer-to-peer music sharing and
other technological advances. The recent agreement between Bertelsmann A.G.’s BMG Entertainment
A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner
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and Napster7 is a step in the direction. Most other music publishers are also acknowledging that they
“have to make buying music easier than stealing music” (Drummond 2000). Following this theme, the
National Music Publishers' association announced a $30 million settlement with MP3.com allowing it
to distribute more than a million commercial tracks using its my.mp3.com service. The music industry
seems to be taking a different approach from that taken by the software industry, which still largely
depends upon legal measures to check piracy, to solve the same problem.
4.1. Implications
• The amount of money saved by using pirated digital music files from online sources has a
significant impact on the club size, as illustrated by the structural equation model (Figure 3). This
implies that availability of free digital music is a major attraction. The indirect implication is that
consumers are highly price-sensitive in the presence of freely available music online, which
suggests the development of pricing models in conjunction with ethical incentives to combat music
piracy.
• An argument that has been put forth states that “downloading” music is not piracy but rather
“sampling.” However, our results show that there exists a relationship between ethical index and
copying (implying that more ethical individuals are less inclined to download online music), which
points to the existence of piracy.
• We found no significant deterrent effect on music piracy through legal and educational campaigns.
Possible reasons for this have been discussed earlier in Section 3. As such, this suggests that
deterrent strategies, will have a limited effect for audio piracy, and the focus should be directed
towards preventive methodologies for diminishing digital music piracy.
7 http://www.bertelsmann.com/press/press_item.cfm?id=2461
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• Some conclusions may be obtained from this study regarding measures that will help check piracy.
For example publishers of hip-hop music are more susceptible to loss of revenue from music piracy
than publishers of other genre. This may also indicate that deterrent messages in the media are best
located in synchronization with hip-hop music.
4.2. Future Research
Digital music piracy studies are just beginning to emerge and there is room for additional
research. For example, in the area of economics, an examination of optimal pricing strategies for music
and conditions under which buyers and sellers are both better off should be studied. Additionally,
pricing models, and their interaction with ethical incentives, are important areas of future research.
One of the most important tasks facing Napster, which focused attention on the digital music piracy
phenomenon, and media giant Bertelsmann, is identifying the subscription rate for their new online
service8. However, the parameters of this service has not yet been publicized9. A study examining the
effect of different public awareness campaigns would be very useful. As noted earlier, is not clear what
type of public awareness campaign will be most effective in combating audio piracy. Finally, a
significant trend is the convergence of software with audio and video. For example, emerging game
software has significant audio and video components. The development of a "unified model" of piracy
would be very valuable in understanding the complex behavioral dynamics of digital piracy as it spans
all areas from biology to business.
8 http://www.thestandard.com/article/display/0,1151,21756,00.html 9 http://www.cnn.com/2001/BUSINESS/09/24/napster/
A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner
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Table 1: Music Demographics Maximum Sum MeanAmount spent on CDs every year 1000 14367 108Amount saved by listening to MP3 songs 1000 13,675 104Legal copies of MP3 songs 1000 3,910 29Pirated copies of MP3 songs 6000 15,573 117Internet use per week 65 1,874 14 Type of music listened to (not mutually exclusive) Hiphop 77 Jazz 33 Electronic 28 Metal 22 Alternative 62 Easy Listening 36 Latin 18 Classic 28 Country 22 Blues 18 Pop an Rock 93
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Table 2: Club Size Items
Doug Watson, an avid listener of music and a computer buff, works as an architect at Architects Unlimited. He recently converted his favorite music CDs into MP3 format (illegally).
• During a holiday family get together, a close family member comes to know about the songs and asks for copies of the MP3 files. Doug Watson emails these files to the family member.
Always Acceptable Never Acceptable
• While Doug Watson is listening to the music at work at Architects Unlimited, one of his colleagues happens to pass by and notices the music. This person is impressed with the quality and the selection of the music on Doug’s computer, and requests a copy. Doug lets him make a copy.
Always Acceptable Never Acceptable
• As more colleagues and acquaintances learn about these music files, Doug Watson decides to make these files publicly available for download from his web site. He encourages others to freely circulate information about his website.
Always Acceptable Never Acceptable
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Table 3: Ethical Index Items
• An executive earning $50,000 a year padded his expense account by about $1500 a year.
Always Acceptable Never Acceptable
• In order to increase profits, a general manager used a production process, which exceeded legal limits for environmental pollution.
Always Acceptable Never Acceptable
• Because of pressure from his brokerage firm, a stockbroker recommended a type of bond, which he did not consider a good investment.
Always Acceptable Never Acceptable
• A small business received one-fourth of its gross revenue in the form of cash. The owner reported only one-half of the cash receipts for income tax purposes
Always Acceptable Never Acceptable
• An engineer discovered what he perceived to be a product design flaw, which constituted a safety hazard. His company declined to correct the flaw. The engineer decided to keep quiet, rather than taking his complaint outside the company.
Always Acceptable Never Acceptable
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Table 4: Ethical Predisposition (Justice)
• All individuals deserve equal treatment before the law. Strongly Disagree Strongly Agree
• Man’s capacity for justice makes democracy possible; but man’s inclination to injustice makes democracy necessary.
Strongly Disagree Strongly Agree
• To no man will we sell, or deny, or delay right or justice. Strongly Disagree Strongly Agree
• All human beings are born free and equal in dignity and rights. Strongly Disagree Strongly Agree
A B
ehav
iora
l Mod
el o
f Dig
ital M
usic
Pira
cy
- Gop
al, S
ande
rs, B
hatta
char
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Agr
awal
, Wag
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Figu
re 1
: Gen
eral
Mod
el o
f Eth
ical
Beh
avio
r
Ethi
cal P
redi
spos
ition
Deo
ntol
olog
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or F
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ores
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•R
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Dem
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phic
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•Gen
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Ethi
cal
Inte
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Ethi
cal
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Org
aniz
atio
nal E
nviro
nmen
t
A B
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ital M
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Pira
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- Gop
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Figu
re 2
: M
odel
of D
igita
l Mus
ic P
irac
y an
d E
thic
s
Ethi
cal P
redi
spos
ition
D
eont
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r For
mal
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• B
elie
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just
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and
law
s Dem
ogra
phic
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Ethi
cal I
nten
tions
•Eth
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Inde
x •C
lub
Size
Mon
ey S
aved
U
sing
MP3
A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner
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Figure 3: Structural Equation Model (Arbuckle 1997)
.00
Justice
.71
Just4
erjust4
.84
.49
just3
erjust3
.70.88
Just2
erjust2
.94.56
Just1
erjust1
.75
errorJustice
.11
EthicalIndex
errorClub Size
.18
.28
ClubSize
errorEthics
Age Gender
-.07
Chi-square = 32.46Degrees of Freedom = 24
P-value = .12Non-normed Fit Index (TLI) = .99
Incremental Fit Index (BL89) = 1.00Comparative Fit Index = 1.00
RMSEA = .05
.02.18.21
-.17
.00
Money SavedUsing MP3
.33
errorSavings
-.34
A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner
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A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner
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