Low Self-Control, Moral Beliefs, and Social Learning Theory in University Students’ Intentions to Pirate Software

Low Self-Control, Moral Beliefs, and Social Learning Theory in University Students ’ Intentions to Pirate Software George E. Higgins and Abby L. Wilson Department of Justice Administration, University of Louisville, Louisville , KY 40292 , U.S.A.

E-mail: [email protected]

Although researchers have examined software piracy using several correlates and theories, it is not clear whether self-control theory and social learning theory have an effect on software piracy that is conditioned by state morals. Using data collected from 318 college students, this study examines the effects that low self-control, differential association, and attitudes have on software piracy and whether morals can condition these effects. The results show that the effects of low self-control, differential association, and attitudes are present in the low moral subsample, but they disappear in the high moral subsample. However, z -tests show that there is no statistically signifi cant difference between these effects in these groups. Security Journal (2006) 19, 75 – 92. doi: 10.1057/palgrave.sj.8350002

Keywords: low self-control ; morality ; software piracy

Introduction

Software piracy has received substantial scholarly attention in recent years ( Britz, 2004 ), which is likely due to the legal and economic implications of this behaviour ( Wall, 2004 ). However, little of this research has been based on criminological theories ( Skinner and Fream, 1997 ; Higgins and Makin, 2004 ). Therefore, the present study examines the condi-tional effects of constructs that come from three crime theories: self-control theory, moral beliefs, and social learning theory. Specifi cally, this study examines the relative impact of low self-control, state moral beliefs, association with deviant peers, and positive attitudes toward software piracy ( Gottfredson and Hirschi, 1990 ; Bachman et al ., 1992 ; Piquero and Tibbetts, 1996 ; Akers, 1998 ; Mazerolle and Piquero, 1998 ; Pratt and Cullen, 2000 ; Rahim et al ., 2001 ). It is predicted that these constructs, which are derived from criminological theory, will add toward understanding software piracy among University students. Specifi -cally, this study predicts that low self-control, differential association, and attitudes will have differential effects on intentions to pirate software at varying levels of state morality among students.

This study contributes to the literature in two ways. First, some of the theoretical reasons why individuals pirate software will be exposed. Second, specifi c information will be learned that criminologists and security specialists may fi nd important in reducing the instances of software piracy.

To make these contributions, the present study begins with a literature review, in which soft-ware piracy is introduced as a white-collar crime and low self-control construct, social learning

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theory, and moral beliefs are applied to software piracy. The methodology for the present study is then explained, after which the results are presented followed by a discussion.

Literature review

Software piracy is a form of white-collar crime that criminologists and security specialists should be able to explain using theory. This section presents the theoretical background and the literature on low self-control, morals, and social learning theory.

Software piracy

Software piracy is the illegal copying of commercially available software in order to avoid fees or the making of unauthorized copies of an organization ’ s internally developed soft-ware for personal use or distribution ( Straub and Collins, 1990 ; Britz, 2004 ). Glass and Wood (1996) argued that growth in personal computer use has paralleled the software piracy problem. Software piracy is a problem because it has signifi cant economic implications and because it is illegal. Economically, acts of software piracy can account for the loss of software sales, jobs, wages, and tax revenue ( Seale et al ., 1998 ; Business Software Alliance, 2003 ; Peace et al ., 2003 ).

The behaviour has been illegal since Congress passed two mandates: the federal Copy-right Act of 1976, as amended in the Computer Software Act of 1980, and Title 17 of the U.S. Code, or the “ shrink-wrap law. ” Under the Copyright act of 1976, the owner of the copyright has the right to reproduce, copy, prepare derivative works, share copies, perform, and display copyrighted material for life plus 50 years (for individuals) or life plus 75 years (for corporations) ( Hollinger and Lanza-Kaduce, 1988 ; Im and Koen, 1990 ). The shrink-wrap law states that when an individual breaks the seal of a software package, the individual is agreeing to accept the terms of the license and to abide by the license ’ s directives ( Im and Koen, 1990 ). Each of these mandates carry with them possible civil penalties ( $ 10,000 per pirated copy of software) and criminal penalties (a possible jail or prison sentence) for copyright violations. These mandates also specifi ed that not only individuals but institu-tions (i.e., corporations, colleges, and universities) may be held liable for the intentional or unintentional software pirating actions of employees and students ( Hollinger and Lanza-Kaduce, 1988 ; Im and Koen, 1990 ; Sims et al ., 1996 ).

Software piracy has civil penalties, but it is a crime – a white-collar crime. A white-col-lar crime is “ an illegal act or series of illegal acts committed by nonphysical means and by concealment or guile, to obtain money or property, to avoid payment of loss of money or property, or to obtain personal or business advantage ” ( Parker, 1986 ). By defi nition, soft-ware piracy meets some of these criteria (i.e., software piracy involves avoiding fees and unauthorized copying to obtain software). In addition, Britz (2004) has commented that software piracy is a behaviour that is diffi cult to detect, which makes it all but impossible to stop. Further, software piracy is committed through nonviolent means that involve ordinary individuals. Also, software piracy is a behaviour that creates substantial economic repercus-sions that criminologists and security specialists need to understand ( Business Software Alliance, 2003 ). Software piracy has the potential to lead to more serious forms of crime or

George E. Higgins and Abby L. Wilson Self-Control, Moral Beliefs, And Social Learning Theory

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deviance in the future ( Hinduja, 2001 ), but it is essentially viewed as a less serious crime because it does not involve physical violence. Therefore, software piracy can be viewed as an emerging form of white-collar crime.

Important to explaining software piracy is an understanding of the environment where it takes place the most and who performs the behaviour. Universities provide a ripe environ-ment for questionable behaviours because rule enforcement is lax, and the students that attend universities usually have little life experience to ground them when situations challenge their morals ( Hinduja, 2003 ). Evidence of this can be seen in some behaviours exhibited by college students, such as academic dishonesty ( Tibbetts and Myers, 1999 ). Software piracy has been shown to take place on University campuses mostly among young male students with computer knowledge or experience ( Hollinger, 1988 ). While this evidence fi rmly establishes software piracy as a student behaviour, the evidence does not use crimino-logical theory to logically organize correlates of the behaviour to provide an understanding of why students pirate software. The next section outlines the theories used in the present study, which have all been shown to be important in students ’ intentions to pirate software. Using these theories, policymakers (i.e., University administrators) may be able to reduce instances of software piracy.

Low self-control

Sherizen (1995) argued that to deter computer crime, which includes software piracy, crimi-nologists and researchers should have an understanding of the individual factors that infl u-ence or promote crime. One individual factor is low self-control – the inability to resist a temptation when an opportunity presents itself ( Gottfredson and Hirschi, 1990 ). To date, one study that used college students has shown that low self-control has a link with software piracy (Higgins and Makin, 2004 ). This link is consistent with previous studies on low self-control (Pratt and Cullen, 2000; Tibbetts and Myers, 1999; Bichler-Robertson et al ., 2003) that have shown that low self-control explains criminal and deviant behaviour, including academic dishonesty.

The link between low self-control and software piracy is consistent with Gottfredson and Hirschi’s (1990) self-control theory. Self-control theory explains the relatively stable dif-ferences in an individual ’ s propensity toward criminal and deviant behaviour. These differ-ences are explained by an individual ’ s level of self-control, which is central to an individual exercising control over his or her immediate desires. Low self-control is viewed as the result of poor or ineffective application of parenting practices at an early age for the child. Once an individual ’ s level of self-control is set, opportunities become important in activating the individual ’ s desire for crime.

Individuals with low self-control are likely to prefer to make decisions impulsively, to prefer simple and easy tasks, to prefer physical instead of mental activities, to prefer risky behaviours, and to be self-centred. Individuals are also likely to be unable to contain their temper. Because of these characteristics, individuals with low self-control often either do not see or disregard the long-term effects of their decisions for themselves and for others ( Gottfredson and Hirschi, 1990 ). The act of software piracy exemplifi es many of the characteristics of low self-control – individuals may not be able to wait to purchase a copy of the software for themselves but instead impulsively pirate a copy. The trust that is established in the licensing


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agreement may not be at the forefront of the individual ’ s mind, thus making it easier to pirate software. Pirating software may provide a thrill for the individual, allowing him or her to de-rive pleasure from the act. Software piracy may be attractive because it is relatively easy and simple to perform. Those pirating a copy of software may only see the benefi t for themselves without regard for others that may be harmed. Thus, the literature ( Hollinger and Lanza-Kaduce, 1988 ) suggests that low self-control has a positive link with software piracy.

Social learning theory

Akers’s (1985, 1998) version of social learning theory involves four concepts: differential association, defi nitions, differential reinforcement / punishment, and imitation. Differential association includes an individual ’ s exposure to criminal behaviour and criminal attitudes through association with others who are involved in crime (e.g., being exposed to others that pirate software). Defi nitions include an individual ’ s positive or negative attitudes toward criminal behaviour, which are rationalizations about the attributes of the criminal behaviour (e.g., positive attitudes toward pirating software). Differential reinforcement refers to the rewards that come from the criminal behaviour (e.g., an individual may gain popularity among his or her peer group for pirating software). Imitation refers to an individual model-ling his or her behaviour after another individual ’ s behaviour (e.g., the individual may use the same techniques to pirate software as another person after watching the individual).

Akers’s (1985, 1998) version of social learning theory is quite complex, requiring re-searchers to measure each concept with a specifi c causal structure in mind. This undertaking is beyond the scope of the current research, but the concepts of social learning theory can provide a foundation to develop hypotheses that are important to software piracy.

Skinner and Fream (1997) showed that differential association and attitudes (i.e., the positive or negative evaluation of computer crime) had positive links with computer crime, especially software piracy. Several people outside of criminology have shown that attitude has a positive link with software piracy ( Eining and Christensen, 1991 ; Reid et al ., 1992 ; Logsdon et al ., 1994 ; Rahim et al ., 2001 ). Therefore, the present study hypothesizes that differential association and defi nitions will have positive links to software piracy.

Moral beliefs

Moral beliefs are concerned with an individual ’ s view of the moral correctness of perform-ing a behaviour and take into account the individual ’ s personal responsibility of his or her actions ( Bachman et al ., 1992 ; Paternoster and Simpson, 1996 ). The present study adopts the view from Paternoster and Simpson – that is, that moral beliefs are part of a normative system that constrains the number of decision options. Further, an individual ’ s moral beliefs are not instrumental in nature. Because moral beliefs are noninstrumental, they are deonto-logical in nature. Under this conception, individuals are constrained from behaviour not be-cause of the individuals ’ or others ’ expected outcomes but because internalized moral rules dictate that certain behaviours should not be performed because they are morally wrong.

Two specifi c implications for behaviour can be drawn from this view. First, moral beliefs constrain behaviour regardless of the cost and benefi ts that may be attached to a behaviour ( Paternoster and Simpson, 1996 ). That is, moral beliefs are independent of any percep-tion of costs and benefi ts. Second, moral beliefs should condition other motivations for


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behaviour – moral beliefs may place some behaviours off limits from performance. Specifi cally, behaviours that are viewed as morally taboo may not be performed no matter the costs and benefi ts of the behaviour. Paternoster and Simpson suggested that this view is consistent with Etzioni ’ s view of moral rules and nonmarkets ( Etzioni, 1988 ; Paternoster and Simpson, 1996 ). Specifi cally, Etzioni suggested that moral beliefs created a nonmarket situation where costs and benefi ts would minimally play a role in the decision to commit crime. Moral be-liefs create an internalized sanction that is at work in the decision to perform a behaviour, which is autonomous from external sanctions. However, the converse of this view also has importance and suggests that there are some market areas where costs and benefi ts will af-fect the decision to perform a behaviour. That is, when an individual judges a situation or behaviour as being feasible (i.e., the behaviour will not counter moral beliefs), the perceived benefi ts and costs become important.

In criminology, moral beliefs have been extensively studied and have been shown to pro-vide a deterrent effect for several behaviours, including sexual assault, assault, and academ-ic dishonesty ( Grasmick and Green, 1980 ; Bachman et al ., 1992 ; Mazerolle and Piquero, 1997 ; Tibbetts and Myers, 1999 ). Moral beliefs have been shown to have a negative link with software piracy ( Higgins and Makin, 2004 ). In addition, some literature has shown that when students consider software piracy unethical, it has a deterrent effect ( Swinyard et al ., 1990 ; Glass and Wood, 1996 ; Seale et al ., 1998 ; Thong and Yap, 1998 ; Wagner and Sanders, 2001 ; Kini et al ., 2003 ). Therefore, the present study hypothesizes that moral beliefs will have a negative link with software piracy. 1

The present study

The present study will go beyond previous fi ndings in the literature by examining the conditioning (i.e., interactive) effects of moral beliefs on low self-control and software

1 One reviewer suggests that using moral beliefs and attitudes as separate measures may create a tautology. We be-lieve that attitudes and morals that are directly measured may be distinct at the operational level. Manstead (2000) argued that direct measures of attitudes and morals may capture different information. That is, attitudes that are measured to evaluate a behaviour as good or bad may capture portions of morals. However, when this evaluation does not take place, attitudes and morals are operationally distinct. That is, when attitudes capture the instrumental parts of evaluations, they become distinct from the noninstrumental nature of morals. For example, when attitude only capture information about the favourable or unfavourableness of a behaviour and not whether the behaviour is good or bad, then attitudes are distinct from morals. Put another way, when attitudes measures are developed in a way that emphasizes the profi tability of a behaviour for an individual rather than the behaviour being good or bad, then the measures are not capturing morals. Therefore, in this format, an individual may hold a positive attitude toward performing a behaviour but still regard the behaviour as being morally wrong. For example, it may be pos-sible for an individual to hold positive attitudes toward software piracy but not see the behaviour as morally correct. Several others have supported our logic in their studies of attitude-behaviour models that incorporate morals (see Conner and Armitage, 1998 ; Conner and Flesch, 2001 ; Conner et al ., 1999, 2003 ; Conner and McMillan, 1999 ; Evans et al ., 2003 ; McMillan and Conner, 2003a, b ; Akers, 1998 , for examples of this usage). Although Akers (1998) suggests that attitudes and morals are evaluative in nature, he would argue for a process that is very similar to the one described here. That is, it is possible for an individual to hold distinctly different attitudes and moral beliefs toward a behaviour. Specifi cally, if attitudes and morals are operationalized using direct measures, and at-titudes capture favourableness and unfavourableness, and moral norms capture morally right and wrong, then they are not capturing the same information and avoid a charge of circular reasoning (i.e., under this view, they avoid the charge of creating a tautology).


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piracy. Based on previous research ( Gottfredson and Hirschi, 1990 ; Bachman et al ., 1992 ; Paternoster and Simpson, 1996 ), the present study will advance the current understanding of the pirating of software by examining the effect of low self-control on software piracy at different levels of moral beliefs. Individuals with low self-control either do not see or they ignore inhibiting factors of behaviours in their lives and pursue immediately gratifying behaviours that they are interested in performing. Therefore, in the present study, it is pre-dicted that low self-control will have the most effect on pirating software for students with relatively low levels of state (i.e., static) moral beliefs toward the behaviour.

This study examines the additive and conditional links of constructs that come from three crime theories: self-control theory, moral beliefs, and social learning theory. Based on self-reports from a nonrandom sample of college students, the study is signifi cant for several reasons. First, it illustrates the conditional links of a prominent individual trait (i.e., low self-control), learning mechanisms (i.e., differential association and defi nitions), and state morality for criminologists and security specialists. Second, it advances criminologists ’ and security specialists ’ understanding of one of the internal mechanisms that may be able to deter software piracy, which could become the subject of policy interventions. Third, the study uses data from a population that commits software piracy so that criminologists and security specialists can better understand why students perform the behaviour.

Method

This section outlines the procedures, sampling, and measures for this study.

Procedures and sampling

Students from an eastern University in the U.S. in the fall 2003 semester voluntarily par-ticipated in the anonymous and confi dential study by completing self-report questionnaires. The students who participated were in four classes in the college of liberal arts (two open to Justice Administration majors and two open to all majors). After being informed of their rights as respondents and the ethical considerations of the study, fi ve students decided not to participate. This set of procedures produced 320 surveys, and after list-wise deletion for missing data 318 surveys remained. 2

The sample had 62 per cent female subjects ( n = 196) and 38 per cent ( n = 122) male subjects. The average age for the sample was 22 years. The sample was 78 per cent white students, 16 per cent African American, and six per cent others (including Hispanic, Native American, and Asian). In comparison to the University from where the sample was drawn, the sample had more African Americans (16 per cent in the sample compared to 11 per cent at the University) and more female subjects (62 per cent in the sample compared to 53 per cent at the University). Important to the current study, the sample ’ s characteristics were

2 The sample development followed the procedures from several published studies from the self-control theory and social learning theory literatures (see Krohn et al ., 1985 ; Nagin and Paternoster, 1993 ; Gibbs and Giever, 1995 ; Piquero and Tibbetts, 1996 ; Higgins, 2002 ) and the software piracy literature (see Hollinger, 1988 ; Solomon and O ’ Brien, 1990 ; Sims et al ., 1996 ; Husted, 2000 ; Hinduja, 2001 ).


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similar to samples of previous software piracy studies (for specifi c details, see Hollinger, 1988 ; Solomon and O ’ Brien, 1990 ; Sims et al ., 1996 ; Husted, 2000 ; Hinduja, 2001 ).

Measures

This section presents the measures of software piracy, low self-control, associating with software pirating peers, software pirating attitudes, moral beliefs toward software piracy, computer use, and control measures.

Software piracy scenario / intentions to pirate software The dependent measure for this study was a scenario about taking software home for per-sonal use and giving it to a friend, taken from Shore et al . (see Appendix A for all of the measures in this study). After reading the scenario, the students were asked to select the like-lihood that they would engage in the behaviour in the scenario on a fi ve-point Likert-type scale (1 = not very likely to 5 = very likely). Higher scores indicated a stronger likelihood to intentions to pirate software under these conditions ( Shore et al ., 2001 ). 3

This measure does not capture an individual ’ s attitude toward software piracy, but it cap-tures an individual ’ s intentions or readiness to perform a behaviour, which some have con-sidered a proxy for actual behaviour ( Fishbein and Ajzen, 1975 ; Ajzen and Fishbein, 1980 ). According to Ajzen (1988) , an individual ’ s attitudes are conceptually and qualitatively dis-tinct from an individual ’ s intentions toward a certain behaviour. That is, intentions represent an individual ’ s motivation to perform a behaviour, whereas an individual ’ s attitudes are their positive or negative evaluation of a particular behaviour. Therefore, some have hypothesized and shown that attitudes are a direct antecedent to behaviour (i.e., intentions were used as a proxy measure for behaviour) and that this link is not tautological ( Conner and Armitage, 1998 ; Sheeran and Taylor, 1999 ; Albarracin et al ., 2001 ; Armitage and Conner, 2001 ; Rivis and Sheeran, 2003 ). Several have capitalized on this structure in criminology ( Nagin and Paternoster, 1993 ; Paternoster and Simpson, 1996 ; Piquero and Tibbetts, 1996 ; Tibbetts, 1997 ; Pogarsky, 2002 ).

Low self-control The measure of low self-control was the 24 item composite Grasmick et al . (1993) scale. The response categories for the scale ranged from one (strongly disagree) to four (strongly agree). Higher scores on this scale signalled lower levels of self-control. 4 This scale had

3 Scenarios provide researchers with an opportunity to capture information in controlled settings across all subjects. Important to Gottfredson and Hirschi’s (1990) theory is opportunity. In this study, the scenarios provide the student with access to the software and the means to pirate the software, making opportunity equal for all of the students in the study ( Bichler-Robertson et al ., 2003 ). As a reviewer pointed out, the computer use measure may be used to control for opportunity. However, in our view, previous research ( Hollinger, 1988 ; Sims et al ., 1996 ; Husted, 2000 ) suggests that computer use is a predictive measure. 4 Some may argue that this measure has problems with validity (see Piquero et al ., 2000 ; DeLisi et al ., 2003 ; Weibe, 2003 ). They would charge that this measure does not form a unitary trait. However, others show that the attitude measure does form a unitary trait (see Nagin and Paternoster, 1993 ; Piquero et al ., 2002 ; Tittle et al ., 2003 ). Fur-ther, some studies show that the measure performs as well as the recommended behavioural measures ( Pratt and Cullen, 2000 ; Tittle et al ., 2003 ; Unnever et al ., 2003 ).


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an internal consistency of 0.83, and principal components factor analysis with a varimax rotation and a Scree test showed the scale was unidimensional, similar to other studies (see Piquero et al ., 2000, 2002; Unnever et al ., 2003 ). The items were then combined to form a low self-control measure, similar to the Grasmick et al . ’ s study.

Differential association The measure of associating with software pirating peers was a composite of six items from Krohn et al . (1985) . The items asked students the following: how many of their: best (male / female) friends copy software; friends (male / female) that they have known the long-est copy software; and friends (male / female) that they are around the most copy software. The students provided this information using fi ve answer choices (1 = none of my friends, 2 = one of my friends, 3 = two of my friends, 4 = three of my friends, 5 = four or more of my friends). Higher scores on the scale represented associating more with software-pirat-ing peers. 5 The scale had an internal consistency of 0.95, and principal components factor analysis using a varimax rotation and a Scree test showed that the scale was unidimensional. The items were then combined to form a measure of differential association.

Defi nitions / attitudes This study used Rahim et al .’s (2001) 11-item scale that captures software-pirating attitudes. In their composite form, the items captured an individual ’ s favourableness or unfavourable-ness toward pirating software, that is, the respondent ’ s view that performing software piracy would be profi table for him or her as opposed to being morally right. Respondents marked their attitude on a four-point Likert-type scale anchored by “ strongly disagree ” to “ strongly agree. ” Higher scores on the scale signalled favourable attitudes toward software piracy. The scale had an internal consistency of 0.89, and principal components factor analysis using a varimax rotation and a Scree test showed that the scale was unidimensional. The items were then combined to form a measure of attitudes toward software piracy.

Moral beliefs To capture the students ’ moral beliefs toward software piracy, this study followed the work of Bachman et al . (1992) and Piquero and Tibbetts (1996) by asking students to respond to a single item: “ how morally wrong would it be to perform the action in the scenario. ” This item captures the noninstrumental parts of evaluation that are distinct from an individual ’ s attitudes. The students marked their response to the item on a fi ve-point Likert-type format. The item was anchored by the response “ not morally wrong ” and “ very wrong. ” When the

5 The questions vary in intensity to gather a more complete understanding of the peers with whom the individual associates. This measure may not accurately capture the full range of differential association measures ( Mazerolle et al ., 2000 ). However, research of differential association contains several studies that use similar measures to these (see Akers et al ., 1979 ; Krohn et al ., 1985 ; Winfree et al ., 1989 ; Skinner and Fream, 1997 ; Reed and Rose, 1998 ). In addition, the measure captures the exposure to deviant attitudes, similar to other studies that use deviant peer association as a control variable in self-control theory (see Evans et al ., 1997 ; Burton et al ., 1998 ; Winfree and Bernat, 1998 ).


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students scored higher on this item, it was a signal of stronger moral beliefs that software piracy is wrong.

Computer use This study used a composite of three items from Igbaria and Chakrabarti (1990) to capture the students ’ computer use. The items asked the students about their use of software (e.g., spreadsheets, word processing, and databases), the Internet, or e-mail. The response catego-ries were: 1 = never, 2 = sometimes, 3 = often, 4 = a lot. The items had an internal consist-ency of 0.74, and principal components factor analysis with a varimax rotation and a Scree test shows that the items form a unidimensional scale. The items were then combined to form a continuous measure of computer use.

Demographic measures Four items provided the demographic measures for this study: the individual ’ s age, race, sex, and major. The age measure was a dichotomous measure that was coded 1 = up to 21 and 2 = 21 and up. The race measure was also a dichotomous measure that was coded 1 for white and 2 for non-white students. The sex measure was a dichotomous measure where 1 represented male and 2 represented female students. The major measure was a dichotomous measure where 1 represented justice administration major and 2 represented nonjustice ad-ministration major.

Results

Table 1 presents the bivariate correlations. These correlations are illustrative of two points. First, there is no multicollinearity among the independent measures. The largest correlation is between attitudes and moral beliefs ( r = 0.50). This is supported by a principal compo-nents factor analysis which shows that attitudes and moral beliefs form two factors. 6 Second, almost all of the correlations are signifi cant, and they are in their hypothesized directions, suggesting that suitable regression analysis is possible.

Table 2 summarizes the multiple regression analysis to determine if the measures from the different theories have a link with software piracy. An important issue to note is that the tolerance and variance infl ation factor (VIF) measures are within suitable ranges (i.e. above 0.20 for tolerance and below 4.00 for VIF, see Fruend and Wilson, 1999 ). This suggests that multicollinearity among these measures is not present. The table shows that low self-con-trol has a small positive link with software piracy. This supports Gottfredson and Hirschi ’ s

6 A principal components factor analysis with a variamax rotation and a Scree test extracted two factors. The fi rst factor contained all of the attitude measures and the factor loadings for this measure ranged from 0.636 to 0.860 for the attitude measures. The factor loading for the second factor contained the moral beliefs measure that had a loading of 0.635. The subsequent factor loadings in factor two were 0.136 to 0.388, which according to Kline (1998) are not high enough be considered in the factor. We are convinced that this is the case when we compared the factor loadings across the two factors. That is, the poor or low factor loadings for factor two were strong and large factor loadings for factor one. This suggests that two factors were extracted: attitudes (factor one) and moral belief (factor two).


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theory and the idea that those who cannot resist the temptation of taking software home and copying it for a friend are likely to do so, similar to previous research. 7

Table 2 also shows that differential association and defi nitions have a positive link with software piracy. Overall, these fi ndings support social learning theory ( Akers, 1985, 1998 ). That is, Akers argued that fi nding support for differential association can be seen as support for social learning theory because it is a global measure of the social learning process. In addition, Akers (1998, 1999) argued that examinations of all of the measures of social learn-ing theory were not necessary and that any signifi cant measure of social learning theory may be seen as support for the theory as a whole.

Further, Table 2 shows that moral beliefs have a negative link with software piracy. This suggests that if individuals see software piracy as morally wrong they are not likely to per-form the behaviour. These fi ndings are similar to previous research ( Swinyard et al ., 1990 ; Glass and Wood, 1996 ; Seale et al ., 1998 ; Thong and Yap, 1998 ; Wagner and Sanders, 2001 ;

Table 1 Sample descriptive statistics and bivariate correlations of measures (n =318)

Mean s.d. 1 2 3 4 5 6 7 8 9

1. Self-control 50.04 7.47 1.002. Differential

association13.12 6.89 0.14a 1.00

3. Attitudes 28.71 5.77 0.31a 0.43a 1.004. Computer

knowledge10.18 2.07 0.00 0.20a 0.14a 1.00

5. Intentions to pirate software

3.29 1.30 0.24a 0.33a 0.50a 0.14a 1.00

6. Moral beliefs 3.25 1.09 −0.08 −0.10 −0.37a 0.05 −0.46a 1.007. Race 1.81 0.39 0.07 −0.05 0.11a −0.01 0.03 0.03 1.008. Major 1.50 0.50 −0.19a −0.04 −0.11 −0.05 −0.13a −0.00 −0.06 1.009. Age 1.40 0.49 −0.08 −0.13a −0.10 −0.04 −0.01 −0.00 −0.09 −0.11a 1.00

aDenotes statistical signifi cance at the 0.05 level.

7 One reviewer suggested that our results were tautological because of this tautology and that the removal of moral beliefs would result in a substantial increase in the magnitude of the attitude measure. We argue that our attitude measure only captures an individual ’ s belief that the behaviour is going to be profi table, whereas our moral meas-ure captures the moral correctness of the behaviour. We argued earlier for a distinction between attitudes and moral beliefs. Further, we agree with the reviewer that removing moral beliefs would increase the magnitude of the atti-tude measure. In fact, when we performed an additional regression analysis taking the reviewer ’ s suggestion, we found that the attitude measure increased as predicted. However, three other measures increased in magnitude as well – low self-control, race, and sex. While our measure of low self-control is an attitude style measure, it does not use software piracy as the content of domain and it follows Akers’s (1991) suggestion for not developing a tautology in studying self-control theory. Further, the race and sex measures, which are not signifi cant when moral beliefs are included in the model, are not signifi cant with larger magnitudes. To us, this suggests that the change in magnitude for attitudes, low self-control, race, and sex is not due to a tautology as the reviewer suggests. We believe that change in the magnitude is the result of a mis-specifi cation of the model because a signifi cant predictor is no longer present. That is, the removal of moral beliefs has allowed other measures to capture signifi cant por-tions of the unexplained variance in intentions to pirate software. Therefore, this is not a demonstration of circular reasoning, but a demonstration of a mis-specifi ed model.


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Kini et al ., 2003 ; Higgins and Makin, 2004 ). In addition, moral beliefs provide the largest effect on software piracy ( B = − 0.35). Importantly, the VIF and tolerance of moral beliefs is well within the acceptable range of no multicollinearity. This suggests that moral beliefs and social learning theory, as measured in the present study, are distinct; therefore, it is important to determine the effect that moral beliefs have on the other measures ’ link with software piracy. 8

To determine if moral beliefs can reduce the effect of low self-control, differential asso-ciation, and defi nitions, a sub-sample analysis was performed by splitting the moral beliefs measure at the median (median = 3) (see Table 3 ). 9 The information in Table 3 shows that multicollinearity is not a problem for either subsamples in this study. That is, the tolerance and VIF measures are within their standards. In the low moral beliefs sub-sample, all three of the measures are signifi cant. The fi ndings from the low moral beliefs sub-sample should be read with caution because the sample size is small. On the other hand, in the high moral beliefs sub-sample, all three of the measures are not signifi cant. In addition to this analysis,

8 Because of the validity and reliability issues of the Grasmick et al . (1993) scale that is presently being debated, additional analysis that examined the links between the subscales revealed that all of the measures had a positive link with software piracy. However, only the link between simple tasks and software piracy is signifi cant. 9 As one reviewer pointed out, it may be necessary to test for interaction effects to assess whether splitting the sample is warranted. However, previous research has shown that the split sample technique is the equivalent of examining if each measure interacts with moral beliefs (see Paternoster et al ., 1998 ; Bichler-Robertson et al ., 2003 ; Gibson et al ., 2004 ). To be sure, we performed a traditional regression with an interaction term between low self-control and moral beliefs. The results were supportive of the split regression analysis. That is, the interaction had a nonsignifi cant link with software piracy ( b = 0.035, B = 0.21, t = 0.177). To us, this provides evidence that the split regression technique provided useful and quality results regarding the interaction between moral beliefs and low self-control.

Table 2 Baseline multiple regression with software piracy as the dependent variable

95% confi dence

b s.e. Beta Interval Tolerance VIF

Self-control 0.02* 0.01 0.12 0.00, 0.04 0.85 1.18Moral beliefs −0.41* 0.06 −0.35 −0.53, −0.31 0.84 1.19Differential association 0.03* 0.01 0.17 0.01, 0.05 0.76 1.32Attitudes 0.06* 0.01 0.25 0.03, 0.08 0.61 1.64Gender −0.04 0.13 0.09 −0.00, 0.09 0.90 1.12Age 0.02 0.01 0.09 −0.00, 0.04 0.94 1.06Computer knowledge 0.03 0.03 0.05 0.00, 0.12 0.93 1.08Race −0.04 0.16 −0.01 −0.35, 0.27 0.95 1.05Major −0.23 0.12 −0.09 −0.47, 0.01 0.93 1.08f 21.25**R2 0.40N 302

*P<0.05 level.**P<0.01 level.


86

a z -test for comparison across groups was used to determine the signifi cant group differ-ences. 10 The z -tests show that there is no difference between the groups.

Discussion

The purpose of the present study was twofold: (1) to examine the links between constructs from three theories (i.e., self-control theory, moral beliefs, and social learning theory) and software piracy and (2) to examine how moral beliefs condition the link between soft-ware piracy and low self-control, differential association, and attitudes. The fi ndings from the study supported the hypotheses and previous literature: low self-control ( Higgins and Makin, 2004 ), differential association, and attitudes had individual positive links to software piracy ( Eining and Christensen, 1991 ; Reid et al ., 1992 ; Logsdon et al ., 1994 ; Skinner and Fream, 1997 ; Rahim et al ., 2001 ), whereas moral beliefs had a negative link with software piracy ( Swinyard et al ., 1990 ; Glass and Wood, 1996 ; Seale et al ., 1998 ; Thong and Yap, 1998 ; Wagner and Sanders, 2001 ; Kini et al ., 2003 ; Higgins and Makin, 2004 ).

This study also fi nds support for the hypothesis that moral beliefs can reduce the link between the theoretical constructs and software piracy. As moral beliefs increase, the link with the theoretical constructs and software piracy disappears. This fi nding is in accord with the view from Bachman et al . (1992) that moral beliefs will inhibit behaviour. However, the z -tests do not show any signifi cant differences between the groups.

10 After splitting the sample to determine if moral beliefs condition the effects of the other independent measures on the intentions of software piracy, an important next step is to determine if these regression coeffi cients are sig-nifi cantly different. To determine if these estimates are statistically signifi cantly different, a z -test that compares the coeffi cients was applied. In the present study, the Paternoster et al . (op cit.) version of this test was used for this analysis.

Table 3 Software piracy: moral belief subsamples

Low moral beliefs 95%Tolerance VIF

High moral beliefs 95%Tolerance VIF z-score

b s.e. BetaCI

b s.e. BetaCI

Self-control 0.03* 0.11 0.16 0.01, 0.05 0.70 1.30 0.01 0.02 −0.06 −0.04, 0.02 0.83 1.21 0.40

Differential association

0.04* 0.10 0.17 0.00, 0.07 0.65 1.53 0.03 0.02 0.22 0.01, 0.05 0.77 1.30 0.00

Attitudes 0.09 0.02 0.36 −0.01, 0.08 0.54 1.86 0.04 0.02 0.22 0.06, 0.12 0.70 1.43 1.67

Gender 0.05 0.03 0.09 −0.37, 0.57 0.85 1.17 0.07 0.04 0.20 −0.42, 0.21 0.88 1.13 −0.40

Age 0.11 0.15 0.04 −0.02, 0.89 0.89 1.13 0.41 0.22 0.20 −0.22, 0.39 0.92 1.08 −1.15

Computer knowledge

0.04 0.04 0.07 −0.07, 0.15 0.77 1.30 0.02 0.05 0.04 −0.02, 0.13 0.93 1.07 0.33

Race 0.02 0.20 0.01 −0.77, 0.31 0.86 1.16 −0.33 0.27 −0.14 −0.31, 0.47 0.94 1.07 1.06

Major −0.16 0.15 −0.06 −0.50, 0.38 0.93 1.07 −0.11 0.22 −0.05 −0.42, 0.19 0.90 1.11 −0.21

F 3.36* 12.59**

R2 0.32 0.28

N 80 223

*P< 0.05 level.

**P< 0.01 level.


87

The fi ndings from this study have implications for policymakers (i.e., college adminis-trators and security specialists). First, the fi ndings suggest that individuals with low self-control who associate with peers that pirate software and who have attitudes favourable to pirating software are an important group to be concerned about. For policymakers and security specialists, this implies that colleges and universities provide a conducive environ-ment where software piracy can take place. Also, these fi ndings provide some indication of the deterrence measures that may reduce instances of software piracy.

Second, the fi ndings show that moral beliefs may be a suitable target for policy because it is a deterrent measure that may help to reduce instances of software piracy. Policymakers and security specialists may be able to develop public service announcements or, because these fi ndings refer to college students, educational messages that try to activate or develop strong beliefs that pirating software is morally wrong. Public service announcements such as these may introduce or reinforce the idea that there are no morally correct justifi cations for software piracy. This will assist in infl uencing perceptions of proper computer behav-iours. Security specialists may be able to incorporate protections into the computer systems for colleges or universities to reduce the opportunity for software piracy. Policymakers and security specialists may develop training that directly outlines that software piracy as a morally incorrect behaviour that is also a crime. Third, the fi ndings from this study suggest that policies developed toward software piracy may have a free-rider effect by reducing the instances of other deviant behaviours.

While the information gained from this study and the policy recommendations appear promising, the study has some limits that deserve attention. The sample is from college students from only one University in the southeastern U.S. This sort of sample restricts the generalizability of the fi ndings because the college students from this University may not be similar to the college students of other universities. However, the present study provides a reasonable fi rst step in understanding how these constructs link to software piracy. On the other hand, the study only uses the Grasmick et al . scale as a measure of low self-control. This is not problematic, as others continue to use the scale successfully ( Grasmick et al ., 1993 ; Tittle et al ., 2003 ). This study only used one measure of software piracy, which is problematic because other forms of software piracy may not provide the same fi ndings. Therefore, this is an area for future research.

Despite limits, the present study provides evidence that criminological theory can explain software piracy. In particular, low self-control, differential association, and attitudes promote software piracy, whereas moral beliefs can reduce the links of these measures with software piracy. Future studies that use different locations and different measures of self-control will be useful in further informing policymakers and security specialists about the results from the present study. For now, the present study supports the premise that individuals pirate soft-ware because of low self-control, differential association, and positive attitudes, while high beliefs that pirating software is morally wrong may reduce its instances of occurrence.

Acknowledgements

We thank Richard Tewksbury for his insightful comments after reviewing a previous draft of this paper. We also thank the anonymous reviewers and the editor Bonnie Fisher for their helpful and guiding comments.


88

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Appendix A

Scenario of Software Piracy

You received approval at work to purchase a $ 125 personal scheduling software package. This package helps you schedule appointments, prioritize jobs that need to be done, and maintain telephone and e-mail lists. Today, you received the package and loaded it onto the PC at work. It looks better than you expected. So, you take the software home and load it on your home computer. A friend visits and admires the scheduling software package. Your friend then asks you to make a copy so she or he can take it home and use it on her or his personal PC.

Software Pirating Peers

How many male friends that you have known the longest copied software without paying for it in the last 12 months? How many of your best male friends copied software in the last 12 months without paying for it? How many of your male friends that you are around the most copied software in the last 12 months without paying for it? How many female friends you have known the longest copied software without paying for it in the last 12 months? How many of your best female friends copied software in the last 12 months without paying for it? How many of the female friends you are around the most copied software in the last 12 months without paying for it?

Attitudes Toward Software Piracy

I do not think it is okay to use copied software because it may create a negative image. I think copied software helps people, including me, save money.


92

I think it is okay to use copied software to improve my productivity. I see nothing wrong in giving friends copies of my software in order to foster friendship. I think it is okay to use copied software if it improves my knowledge. I think it is okay to use copied software because the community at large is eventually ben-efi ted. I believe that copying software helps to increase my computer literacy. I think it is okay to use copied games software for entertainment. I see nothing wrong in using copied software if it is badly needed for the success of a project. I think it is okay to use copied software for research purposes, because everybody shares the benefi ts. I think copying software is okay to punish software publishers who charge high prices.

Computer Use

How many times in the last 2 weeks did you use personal computer software packages, such as spreadsheets, word processing (e.g., Word, Wordperfect, etc.), or databases? How many times in the last 2 weeks have you used the Internet? How many times in the last 2 weeks did you use your e-mail?

Moral Beliefs

How morally wrong is it to perform the action in the scenario?

Documents

Low Self-Control, Moral Beliefs, and Social Learning Theory in University Students’ Intentions to Pirate Software