A Behavioral Model of Digital Music Piracy€¦ · 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

(Forthcoming in Journal of Organizational Computing and Electronic Commerce)

© Journal of Organizational Computing and Electronic Commerce

A Behavioral Model of Digital Music Piracy (Forthcoming in Journal of Organizational Computing and Electronic Commerce)

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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.

• CDs can be created that contain over 160 compressed digital music files that can play for over 14 hours

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.


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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

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. 4 IFPI’s Music Piracy Report 2000. 1999 IFPI, http://www.ifpi.org.


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and income effect related to 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 recording

companies would quite likely have to sue individual customers, a prospect they have tried to avoid (Ahlberg

2000, Bravin 2000, Clark 2000).


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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 1996; Gopal and

Sanders 1997; Jerry 1987; Mason 1990; Solomon and O'Brien 1991). The enormous impact of software

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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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 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


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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 “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


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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 through a better understanding of

individual ethical behavior.


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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 herself) at home,


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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 to

operationalize the Justice construct, a latent variable, as an ethical predisposition towards laws and the justice


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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 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


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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).

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


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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 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

6 Note that higher values for the Ethical Index imply being more ethical and higher values for the Club Size imply being less


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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 true news story from a

widely distributed university newspaper.

Web Pirate: Copying Downloads For Friends? Get Out Your Checkbook

likely to form a pirating club and to engage in pirating MP3 files.


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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 legal ramifications. This issue needs to be investigated further to devise an

effective deterrent strategy to check music piracy.


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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 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


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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 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

7 http://www.bertelsmann.com/press/press_item.cfm?id=2461


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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.

• 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.


21

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


23

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 r r r r r r r 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.



24

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.



25

Table 4: Ethical Predisposition (Justice)

• All individuals deserve equal treatment before the law. Strongly Disagree r r r r r r r Strongly Agree

• Man’s capacity for justice makes democracy possible; but man’s inclination to injustice makes democracy necessary.

Strongly Disagree r r r r r r r Strongly Agree

• To no man will we sell, or deny, or delay right or justice. Strongly Disagree r r r r r r r Strongly Agree

• All human beings are born free and equal in dignity and rights. Strongly Disagree r r r r r r r Strongly Agree


26

Figure 1: General Model of Ethical Behavior

Ethical Predisposition Deontolological or Formalistic

•Justice and laws •Ideals •Customs •Mores

Consequential or Teleological •Utilitarianism •Egoism •Relativism

Demographics •Age •Gender

Ethical Intentions

Ethical Behavior

Cultural Environment

Organizational Environment


27

Figure 2: Model of Digital Music Piracy and Ethics

Ethical Predisposition Deontolological or Formalistic

• Belief in justice and laws

Demographics •Age •Gender

Ethical Intentions

•Ethical Index •Club Size

Money Saved Using MP3


28

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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