A behavioral model of digital music piracy

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


Suzanne C. Wagner Niagara University

Dept. of Computer Information Sciences Niagara University, NY 14109-2019


*: Corresponding Author

Revised January 2002

A Behavioral Model of Digital Music Piracy - Gopal, Sanders, Bhattacharjee, Agrawal, Wagner

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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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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


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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


21

• 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/


22

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.


• 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.



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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.


• In order to increase profits, a general manager used a production process, which exceeded legal limits for environmental pollution.


• Because of pressure from his brokerage firm, a stockbroker recommended a type of bond, which he did not consider a good investment.


• 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


• 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.



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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

jee,

Agr

awal

, Wag

ner

26

Figu

re 1

: Gen

eral

Mod

el o

f Eth

ical

Beh

avio

r

Ethi

cal P

redi

spos

ition

Deo

ntol

olog

ical

or F

orm

alis

tic•J

ustic

e an

d la

ws

•Ide

als

•Cus

tom

s •M

ores

C

onse

quen

tial o

r Tel

eolo

gica

l •U

tilita

riani

sm

•Ego

ism

•R

elat

ivis

m

Dem

ogra

phic

s •A

ge

•Gen

der

Ethi

cal

Inte

ntio

ns

Ethi

cal

Beh

avio

r

Cul

tura

l Env

ironm

ent

Org

aniz

atio

nal E

nviro

nmen

t

A B

ehav

iora

l Mod

el o

f Dig

ital M

usic

Pira

cy

- Gop

al, S

ande

rs, B

hatta

char

jee,

Agr

awal

, Wag

ner

27

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

olol

ogic

al o

r For

mal

istic

• B

elie

f in

just

ice

and

law

s Dem

ogra

phic

s •A

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•Gen

der

Ethi

cal I

nten

tions

•Eth

ical

Inde

x •C

lub

Size

Mon

ey S

aved

U

sing

MP3


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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


29


30

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A behavioral model of digital music piracy

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