Exploring antecedents of attitude and intention toward Internet piracy among college students in South Korea
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- Cite this article as:
- Khang, H., Ki, E., Park, I. et al. Asian J Bus Ethics (2012) 1: 177. doi:10.1007/s13520-012-0017-5
This study aims to examine the predictors of attitude and intentions toward Internet piracy in South Korea. Also, it intends to suggest a model of Internet piracy demonstrating the casual effects of factors of individual attitude and intentions toward Internet piracy. The results demonstrated that moral obligations and subjective norms are significant predictors of an individual’s attitude toward Internet piracy. Moreover, three factors—moral obligation, perceived behavioral control, and attitude—are essential antecedents of an individual’s intention to engage in Internet piracy. The findings of this study embrace multiple implications for factors affecting piracy and promote future research around this topic.
KeywordsInternet piracySouth KoreaAttitudeIntention
Although new technology has positively advanced both our individual lives and society as a whole, such advancements are not without drawbacks. Due to fast Internet connections, the availability of affordable high capacity storage, and underground Internet peer-to-peer networks, the replication and distribution of products or services without the permission of copyright owners is now easier and faster than ever before (Cronan and Al-Rafee 2008). Moreover, diverse mobile devices including cellular phones, palm devices, and flash drives have contributed to the ubiquity of piracy (United States Trade Representative 2010).
The impacts of Internet piracy1 have become significant. For example, in 2008, over 40% of the personal computer software used worldwide was unauthorized, resulting in revenue losses surpassing the $50 billion mark for the first time (Business Software 2010). Similarly, one in every three music CDs and cassettes sold worldwide is pirated, and the estimated value of the pirated music market in 2005 was at least $4.5 billion (IFPI06 2006). Moreover, the Motion Picture Association of America estimated that in the same year, approximately a half million movies were copied and downloaded on the Internet everyday (MPAA 2005), which translated to an $18.2 billion loss to the worldwide movie industry in 2005.
In response to these issues, multiple organizations have been attempting to combat Internet piracy. For example, the Motion Picture Association of America (MPAA) has investigated more than 34,000 cases of piracy and assisted law enforcement officials in conducting more than 10,500 raids. The MPAA has also launched a global publicity campaign to emphasize their message that piracy is a crime and to increase public awareness of piracy activities.
While creative producers and related organizations have made efforts to fight Internet piracy, scholars have endeavored to identify determinants of piracy behaviors acting on individual (Al-Rafee and Cronan 2006; Chiou et al. 2005; Cronan and Al-Rafee 2008) and cross-cultural levels (e.g., Ki et al. 2006; Swinyard et al. 1990). Despite the magnitude of the problem, however, there has been relatively little research done to examine predictors of an individual’s engagement in Internet piracy on a local level. In fact, Internet piracy has been a significant concern in a number of countries, including Brazil, Canada, China, India, Italy, Russia, Spain, and Ukraine (U.S. Trade Representative 2010).
South Korea, in particular, stands out as an interesting country to explore the issue of Internet piracy, since the country provides the fastest Internet connections in the world (McDonald 2011) and has been identified as a notorious market where numerous webhards2 operate in Korean Web space to provide illegal content (U.S. Trade Representative 2010). In addition, South Korea is the country with the highest rate of Internet penetration in the world, reaching 81.1%, though only 39.4 million people in the country use the Internet (Internet World 2010). This study specifically examines determinants of individual decision making pertaining to Internet piracy in South Korea.
In the Korean local context, this study aims to examine predictors of attitude and intentions toward Internet piracy. To achieve the objective, this study adopted varied antecedents of Internet piracy from previous studies that applied theories of planned behavior or reasoned action with additional variables (e.g., demographics, moral obligations (MO), perceived prosecution risk (PPR), etc.) to predict a wide range of immoral behaviors (e.g., Beck and Ajzen 1991; Cronan and Al-Rafee 2008). With those predictors identified, this study intends to suggest a model of Internet piracy demonstrating the casual effects of factors of individual attitude and intentions toward Internet piracy. The findings of this study are expected to provide a better understanding of such factors on a local level. In addition, findings will inform the development of strategic decision making and the creation of effective persuasive messages for a public campaign to protect copyrights and intellectual property.
Theory of planned behavior
The theory of planned behavior has been employed in studies examining factors affecting behavioral intention. This theory posits that three independent variables—attitude, subjective norm, perceived behavioral control—determine intentions toward the behavior in question (Ajzen 1991). It maintains, in particular, that all these variables are positively associated with behavioral intention. That is, the more favorable the attitude and subjective norm toward a behavior and the greater the perceived behavioral control, the stronger an individual’s intention will be to engage in the behavior in question. Though theory of planned behavior (TPB) has garnered significant empirical evidence supporting the theory, still relatively less research has drawn upon this theory as compared to its predecessor, the theory of reasoned action (TRA). For example, Ajzen (1991) reviewed 16 studies that applied TPB and confirmed the sufficient predictive power of the model. Also, Parker et al. (1992) used both TRA and TPB to predict driving violations and determined that the addition of perceived behavior control (PBC) significantly increased the amount of variance explained. Based primarily on TPB and drawing upon several predictors identified in previous literature (Ajzen 1991; Cronan and Al-Rafee 2008; Parker et al. 1992), the present research examines the effects of six variables—gender, moral obligations, subjective norms, attitude, perceived behavior control, and perceived prosecution risk—on individuals’ intentions toward Internet piracy.
Gender has been tested as a predictor of immoral behaviors such as piracy, though findings remain indecisive regarding its impact. A group of studies have reported that gender does little to explain immoral behaviors (e.g., Taylor and Shim 1993; Wong et al. 1990. For example, Taylor and Shim (1993) examined the effect of gender on software piracy and found no relationship between the two variables. In contrast, multiple studies have argued that gender represents a meaningful predictor of ethical behavior (e.g., Leonard and Cronan 2001; Reiss and Mitra 1998; Simpson et al. 1994). For example, Simpson et al. (1994) indicated that gender is an important variable influencing individuals’ propensity toward partaking in software piracy. In studies that identified gender as a significant predictor, females generally demonstrated greater sensitivity to unethical activities than males (Ford and Richardson 1994; Pereira and Kanekar 1994). Computer use tends to be considered a masculine activity, and male students are generally more passionate about computers and spend more time using computers than their female counterparts do (Williams et al. 1994). This substantial use of computers by males may increase the likelihood of engaging in piracy activities.
Gender and moral obligation
- Hypothesis 1:
Females will demonstrate higher levels of moral obligation than males.
Gender and attitude
- Hypothesis 2:
Females will possess more negative attitudes toward Internet piracy than males.
Moral obligation and attitude
- Hypothesis 3:
Individuals with higher levels of moral obligation will demonstrate more negative attitudes toward Internet piracy.
Moral obligation and behavioral intention
- Hypothesis 4:
Individuals with higher levels of moral obligation will demonstrate less intention of participating in Internet piracy.
Subjective norms and attitude
- Hypothesis 5:
Individuals with higher levels of subjective norms (not to perform the Internet piracy) will demonstrate more negative attitudes toward Internet piracy.
Subjective norms and intention
- Hypothesis 6:
Individuals with higher levels of subjective norms (not to perform the Internet piracy) will demonstrate less intention of participating in Internet piracy.
Perceived behavior control
- Hypothesis 7:
Individuals with higher levels of perceived behavior control will demonstrate greater intentions for participating in Internet piracy.
Perceived prosecution risk
- Hypothesis 8:
Individuals who perceive high levels of prosecution risk will demonstrate less intention of participating in Internet piracy.
Attitudes and behavioral intention
- Hypothesis 9:
An individual’s attitude toward Internet piracy will positively influence his/her intention of participating in Internet piracy.
This study employed a self-administered survey for data collection. Respondents included undergraduate and graduate students enrolled in three large universities in Seoul, South Korea. College students were specifically chosen as the sample for this study because a high proportion of college students have been found to be involved in Internet piracy due to the combined factors of their financial status and strong computer skills (Liang and Yan 2005; Sims et al. 1996).
The original sample consisted of 451 college students. In the process of data mining, 73 incomplete responses were removed, resulting in a final study sample of 378 for analysis. The sample was comprised of 197 female (52%) and 181 male respondents (48%) with an average age of 22 years old.
To test the proposed hypotheses, attitude (for H2, H3, H4), behavioral intention (for H5, H6, H7, H8, H9), and moral obligation (for H1; however, for H3 and H9, this variable was used as independent variable) served as dependent variables. A seven-point Likert scale ranging from “strongly disagree (1)” to “strongly agree (7)” was employed to measure all variables with the exception of gender.
Participants were asked to indicate their gender. For data analysis purposes, this variable was coded as a dummy variable with “0” used for “male” and “1” for “female.”
This variable relates to one’s feelings of guilt or obligation to perform or not to perform Internet piracy, and it was treated as an independent variable for H3 and H9 and as a dependent variable for H1. Participants were asked to respond to three statements, such as “Internet piracy goes against my principles” and “It would be morally wrong to pirate materials on the Internet.” Cronbach’s alpha for the initial measures of moral obligation was found to be 0.56.
This study defines subjective norms as one’s perceptions of social pressure to perform or not perform Internet piracy (Ajzen 1985). Participants were asked to indicate what kind of opinions they believed their significant others held regarding Internet piracy as well as their personal desires to adhere to how these significant others would like for them to behave. Three items were used to measure subjective norms, including statements such as “Most people who are important to me think I should not pirate material on the Internet” and “When considering Internet piracy, I wish to do what people who are important to me would like me to do.” Cronbach’s alpha for the initial measures of subjective norms was calculated at 0.71.
Perceived behavior control
This study refers perceived behavior control as an individual’s perception of his/her ability and opportunity to commit Internet piracy. Participants were asked to indicate their perceived resources, abilities, or opportunities for committing Internet piracy by considering five statements, including “If I wanted to, I could easily pirate material on the Internet,” “I believe that I have the ability to pirate material on the Internet,” and “I have the resources necessary to pirate material on the Internet.” Cronbach’s alpha for the initial measures of this variable was 0.95.
Perceived prosecution risk
This study defines PPR as the degree to which one perceives potentially negative outcomes as a result of performing Internet piracy. This variable was measured by asking participants to respond to the following two statements: “By law, Internet piracy is illegal and will be punished” and “Internet piracy will be caught for copyright infringement.” Cronbach’s alpha for the initial measures of perceived prosecution risk was 0.93.
Attitude toward Internet piracy
This variable was assessed with items eliciting respondents’ overall favorable or unfavorable attitudes toward Internet piracy behavior. In particular, participants were asked to respond to four semantic differential items including favorable/unfavorable, beneficial/harmful, wise/foolish, and good/bad along a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). Cronbach’s alpha for the initial measure of attitude was 0.80.
Intention toward Internet piracy
This variable was measured by asking participants to respond to three statements, including “I intend to pirate material on the Internet in the near future” and “I will try to pirate material on the Internet in the near future.” Cronbach’s alpha for the initial measures of intentions was 0.97.
While no universal criteria exists for testing the reliability of measurement scales, one of the most widely accepted rules of thumb is that a scale’s alpha should be at least 0.70 to demonstrate internal consistency (Nunnally 1978).3 Thus, with the exception of moral obligation, all of the initial measures met this criterion.
Exploratory factor analysis
Prior to testing the proposed model, scale purification was conducted through exploratory factor analysis (EFA). EFA established a preliminary version of all measures by identifying items with low factor loadings and determining whether each measurement loaded on its intended factor. The major purpose of this step was to evaluate the dimensionality and the appropriateness of the measurement items for each variable. A measurement item was excluded if it (1) was extracted as the second factor of the intended factor, (2) displayed opposite signs of factor loading coefficients among the other items in the intended factors, and (3) had factor loading values of less than 0.65 with other items of the respective subscale (Hair et al. 1998).
To test the nine hypotheses, this study performed two types of statistical analyses—regression analysis and path analysis. Two multiple regression analyses were used to test all of the hypotheses with the exception of H1. Specifically, the effects of gender, moral obligation, and subjective norms on attitude toward Internet piracy were tested simultaneously through the first regression analysis (hypotheses 2, 3, and 5). The second regression analysis tested the effects of moral obligation, subjective norms, perceived behavior control, perceived prosecution risk, and attitude on piracy intention (hypotheses 4, 6, 7, 8, and 9).
Attitude toward the Internet piracy = α1 + β1Gender + β2Moral Obligation + β3Subjective norms
Internet Piracy Intention = α2 + β4Moral Obligation + β5Subjective norms + β6Perceived Behavior Control + β7Perceived Prosecution Risk + β8Attitude
Although regression analysis is commonly used to test the effect of multiple independent variables on a dependent variable and displays easily understandable results, such analysis does pose some drawbacks. First, regression analysis cannot test the endogeneity of mediating variables. For example, hypotheses 2, 3, and 5 aimed to test the relationship between mediating variables, so this study performed path analysis, an extension of the regression analysis (Hair et al. 1998). Second, though regression analysis possesses a function to display the degree of multicolinearity among the independent variables, it cannot manage the issue. Path analysis may offer a viable option to control for this problem as it can test interdependent relationships among independent variables. To test the proposed hypotheses in one model, this study used a goodness-of-fit test from the structural equation modeling program AMOS 7, which computes multiple alternatives of goodness-of-fit coefficients. The goodness of fit was calculated by entering the path model and its data into the software package. In summary, the use of multiple statistical analyses increased the anticipated robustness of this study’s results.
The descriptive statistics demonstrated interesting insight into the variation of data and the means and standard deviation of the respective variables. For the averages of attitude toward the Internet piracy and Internet piracy intention, the dependent variables were 4.54 (SD = 1.24) and 4.42 (SD = 1.81), respectively, along the seven-point scale. Among the variables used in this study, respondents demonstrated the highest mean score for perceived behavioral control (M = 4.82, SD = 1.54). This indicates that respondents seem to believe that they possess ability or control in the context of Internet piracy situations. The respondents tended to perceive lower levels of social pressure against engaging in Internet piracy and moderate levels of moral obligation and perceived prosecution risk (M = 2.91, 3.89, and 3.68, respectively).
Exploratory factor analysis
The variables with multiple items (three items for behavioral intention, four items for attitude, three items for subjective norms, five items for perceived behavior control, three items for moral obligation, and two items for perceived prosecution risk) were analyzed. The results of EFA indicated that none of the measurement items needed to be removed. Therefore, all initial measures were used to run further statistical analysis. This study used composite scores produced from principal component analysis for each latent variable with multiple measurement items.
The second regression analysis was performed with Internet piracy intention as a dependent variable. The total variance accounted for was 41%, and this model was highly significant in terms of F values (F = 42.881, p < .001). Among the five independent variables tested, moral obligation (β = −0.10, p < 0.05), perceived behavior control (β = 0.37, p < .001), and attitude (β = 0.36, p < .001) appeared to be important precursors of Internet piracy intention. However, subjective norms and perceived prosecution risk were not found to be significant. The outcome of the second regression analysis supported hypothesis 4, which anticipated that moral obligation would negatively affect Internet piracy intention; hypothesis 7, which predicted that perceived behavior control would be positively related to Internet piracy intention; and hypothesis 9, which expected a positive relationship between attitude toward Internet piracy and Internet piracy intention. However, the results of the second regression analysis rejected hypothesis 6, which predicted the relationship between subjective norms and Internet piracy intention, and hypothesis 8, which anticipated the relationship between perceived prosecution risk and Internet piracy intention.
Path analysis of proposed research model
Gender → MO
Gender → Attitude
MO → Attitude
SN → Attitude
Attitude → Intention
SN → Intention
PBC → Intention
PPR → Intention
MO → Intention
The effects of the independent variables on attitude toward Internet piracy and Internet piracy intention were tested together with the interdependent relationship between gender, moral obligation, and attitude. A path analysis with maximum likelihood estimation showed that attitude was affected by moral obligation (H3) (β = −0.42, p < .001) and subjective norms (H5) (β = −0.26, p < .001). This result indicated that individuals with higher levels of moral obligations tend to demonstrate more negative attitudes toward Internet piracy (H3). Regarding H5, the findings demonstrated that an individual’s attitude toward Internet piracy is likely to be influenced by significant others or social pressure. In other words, when an individual feels greater social pressure not to engage in Internet piracy behavior, s/he would also be more likely to demonstrate negative attitudes toward such behavior. This outcome demonstrates good fit with the results of the regression analysis because the same independent variables were determined to be significant in both analyses. Thus, hypotheses 3 and 5 were supported. On the other hand, gender did not display a significant direct relationship to either moral obligation or attitude toward Internet piracy. Therefore, hypotheses 1 and 2 were rejected in the path analysis.
The same analysis displayed that Internet piracy intention was affected by moral obligation (H4) (β = −0.10, p < 0.05), perceived behavior control (H7) (β = 0.38, p < .001), and attitude (H9) (β = 0.36, p < .001). Results supported hypothesis 4, thus indicating that an individual demonstrating higher level of moral obligation tends to exhibit less intention to participate in Internet piracy. Regarding H7, the finding suggested that individuals with greater perceived behavior control were more likely to display greater intentions to partake in Internet piracy. Also, as expected, attitude toward Internet piracy was found to be a determinant of Internet piracy intention. Therefore, hypotheses 4, 7, and 9 were supported. On the other hand, neither subjective norms nor perceived prosecution risk were found to be significant predictors of Internet piracy intention. Therefore, hypotheses 6 and 8 were rejected in both analyses.
Goodness-of-fit indices: the proposed model
Comparative fit index
Normed fit index
Root mean squared error residual
Discussions and conclusions
This study provides greater understanding and insights into the influential predictors of attitudes toward Internet piracy as well as intentions of participating in Internet piracy among South Korean college students. The two main analyses, regression and path, produced both interesting and consistent results. The results demonstrated that moral obligations and subjective norms are significant predictors of an individual’s attitude toward Internet piracy. Moreover, three factors—moral obligation, perceived behavioral control, and attitude—are essential antecedents of an individual’s intention to engage in Internet piracy. Figure 2 represents the outcome of the model tested in this study. The results of this study embrace multiple implications for factors affecting piracy and promote future research around this topic.
In examining moral obligation as a predictor of attitude and intention toward Internet piracy, the findings suggested that an individual’s moral obligation acts as an important antecedent of his/her attitudes and intentions toward Internet piracy. The notion of moral obligations maintains that individuals experience personal feelings of guilt that can influence their attitudes and intentions toward Internet piracy. These feelings of guilt likely play a role in forming individuals’ attitudes, which tend to transform into behavioral intentions. For example, this finding can be utilized as a method to deter Internet piracy by underscoring the threat of undesirable consequences through a variety of campaign messages. It is therefore assumed that individuals exposed often to such messages would be more likely to demonstrate unfavorable attitudes toward Internet piracy. Moreover, they would be more likely to feel guilty if they attempted to pirate materials on the Internet.
Subjective norms represent an important notion when predicting attitudes toward Internet piracy. It is logical to assume that reference groups that students perceive as important in their daily routines, including parents, friends or teachers, play an influential role in their formation of attitudes toward Internet piracy. In other words, these influential people may represent the “social pressure” necessary to cultivate either positive or negative attitudes toward Internet piracy among the students. Accordingly, this research suggests targeting these important reference groups when planning a campaign strategy or creating a persuasive message against Internet piracy. Role models, such as parents in the home or teachers in the schools, could represent a good starting point for effectively instilling negative attitudes toward Internet piracy in students.
Unlike in previous studies (Chang 1998; Cronan and Al-Rafee 2008), however, the subjective norm was not found to be an important predictor of piracy intention. This counterintuitive finding demonstrates that people are likely to construct negative attitudes toward Internet piracy when they are aware of reference groups’ perceptions that they should not engage in piracy; however, such awareness is unnecessary in preventing these individuals from attenuating intention toward piracy or eventually partaking in pirating behavior. Considering that many studies have found attitude to be the best predictor of an intention to partake in a specific behavior (Al-Rafee and Cronan 2006), which was further confirmed by the outcome of the path analysis, this study can concede that there may be an indirect effect of the subjective norms on piracy intention via attitude. Also, subjective norms alone represent a scant factor in deterring piracy behavior in the Korean context. Consequently, this hypothesis should be addressed in future research, which could yield interesting and informative results.
Perceived behavior control was found to be a significant predictor of piracy intention among Korean students. Perceived behavior control refers to an individual’s perception of his/her ability and opportunity to engage in the behavior (Ajzen 1991). This is an important factor to consider in the context of South Korea, which furnishes the world’s fastest Internet connections and plans to connect every home to the Internet at one gigabit per second, or more than 200 times the average household setup speed in the USA (McDonald 2011). The infrastructure of Internet service can provide Korean students ample opportunities for involvement in Internet piracy, and they seem to perceive Internet piracy activity to be a relatively easy process in the context of the given system. Moreover, Korean students appear well aware of their abilities and available resources for committing Internet piracy.
As mentioned earlier, many studies have found attitude to be the most significant predictor affecting behavioral intention (Trafimow and Sheeran 1998), and the findings of this study further support the idea that attitude toward Internet piracy is closely associated with individuals’ intentions of engaging in Internet piracy. Attitude has been vigorously studied since individual attitude is considered an important construct in predicting one’s behavioral intention (Al-Rafee and Cronan 2006), which in turn has been found to be an accurate variable for predicting behavior (Ajzen 1985). In certain conditions where subjective norms operate or an individual is ambivalent about a certain issue, however, attitudes are less likely to translate into actual behavior (Perloff 2003). Thus, significant antecedents of attitude, such as subjective norms or moral obligation, tend to be factors more concerned with creating persuasive messages against the piracy.
A couple of this study’s observations are noteworthy. First, perceived prosecution risk was not found to be significant in predicting attitudes or intentions toward Internet piracy, a finding that differs from those of previous studies (Tan 2002). This finding suggests that Korean students are less sensitive to the negative consequences of copying illegal materials on the Internet. Woo’s (2003) study argued that although a digital copyright case on file-sharing service strives for controlling Internet piracy more strictly, the case demonstrated a limited influence on Koreans in altering their perceptions of piracy. Along with the public campaign against piracy, more press release activities regarding copyright infringements and cases are therefore encouraged in order to increase awareness of the prosecution of Internet pirates, especially in Korea.
Second, the findings indicate that gender is not considered a significant antecedent of either moral obligation or attitude in South Korea. As discussed earlier, there have been mixed findings regarding the significance of the individual attribute on piracy or immoral behaviors. In some studies, findings have shown that females tend to be more sensitive than males to unethical behaviors (Leonard and Cronan 2001; Reiss and Mitra 1998), while others have reported that gender does little to explain immoral behaviors (Taylor and Shim 1993; Wong et al. 1990). The results of this study are in line with the latter perspectives, but further examination of this variable is encouraged in future studies, as it is unclear whether this study’s finding is limited to the Korean local context or is generalizable as a universal norm.
Limitations and future research directions
As is true with much research, this study has several limitations that should be considered in helping to inform future research endeavors. Several of this study’s findings are considered original and compelling, but they should be interpreted with caution. First, a non-probability sampling method was used to collect the sample. Although the study offered rationales for selecting college students as the sample, the findings of this research may therefore lack generalizability. Thus, more research is required to validate the proposed model. Second, this study is limited to a local context to determine factors affecting attitudes or intentions toward Internet piracy. Future studies are encouraged to include multiple countries in efforts to compare this study’s results with those found in different cultural contexts. Such multi-country studies would contribute to identification of a universal measurement scale.
In this study, Internet piracy refers to any behavior involving illegal copying and/or distribution of copyrighted media such as software, movies, music, television programs, book files, etc. over the Internet without the copyright owner’s permission.
Webhards are web-based storage services that offer high-volume storage space for sharing of pirated material.