Digital Piracy: Factors that Influence the Intention to Pirate

Digital Piracy: Factors that Influence the Intention to Pirate By Rúben Emanuel Moutinho Meireles Master’s Dissertation in Economics and Business Administration Supervised by: Professor Doctor Pedro José Ramos Moreira de Campos 2015 i Biographical Note Rúben Emanuel Moutinho Meireles born on April 5th 1992, attended Escola Básica 2,3 C/ Secundário de Carrazeda de Ansiães where he graduated in 2010. On that same year enrolled in Faculty of Economics of the University of Coimbra, where obtained his bachelor degree in Economics in the summer of 2013. In September 2013, seek out to obtain his Master degree in Economics and Business Administration at the University of Porto, Faculty of Economics, finishing the academic part in July 2015 and presenting now this dissertation. ii Acknowledgements To my family, indeed time truly passes by, it seems like was only yesterday that this educational journey was starting, and today it is in part the end of it. I am truly grateful for all the opportunities given to me, without your help, encouragement and support none of this could be possible, but most importantly I would not be you I am. I would like say thank you to all of my friends. You guys know who you are and I am truly glad for the support, patience, participation, and most of all simply for being present. To my dissertation advisor, Prof. Dr. Pedro Campos I am grateful for the opportunity to work with you and appreciate the time and effort dedicated. Also, thank you for the suggestions, advices and knowledge transmitted. I am much obliged to all the people that filled out the questionnaire, all of you made a huge contribution. However, a special thank you has to go to Prof. Maria Matias. iii Abstract This dissertation uses behavioral and economic theories to help understand some of the factors (attitude, subjective norms, perceived behavioral control, moral obligation, past piracy behavior, punishment severity, punishment certainty, digital media cost and perceived value) that may influence an individual’s intention to pirate digital material. It is used an expanded framework based on the theory of planed behavior, addressing not only factors capable of influencing intention, but also using antecedents of these factors, capable of influence intention in an indirect fashion. This work assists to fulfill the need to study digital piracy across different cultures, helping to understand how intention is differently affected and how policy makers should adjust policies between cultures. Although most of the factors employed are not new in piracy research there is an exception, perceived value, this factor was never analyzed in this context. Another innovation of this work is the development of two models: the first one considers the full sample and the second considers only those who had pirated before. A student sample has been used and the data was analyzed using structural equation modeling. There were some different results between the models however, the factors perceived behavioral control and moral obligation were significant predictors of intention in both models, but subjective norms only presented a significant effect in the full sample model. Punishment certainty was also a significant predictor of perceived behavioral control in both models. Among the factors that were not significant predictors of intention was attitude. Its antecedents also showed some mixed results, punishment certainty and severity did not present a significant effect in both models, however digital media cost and perceived value were significant predictors of attitude but only in the full model. The pirate model confirmed the existence of a significant and strong relation between past behavior and intention towards digital piracy. The results and implications are discussed and forms of intervention are suggested. iv Resumo A presente dissertação tem como objetivo fornecer um melhor entendimento sobre alguns dos fatores que podem afetar a intenção de um indivíduo piratear. Usando a theory of planned behavior como ponto de partida e complementando-a com outras teorias e variáveis relevantes, foram desenvolvidos dois modelos capazes de analisar a intenção de piratear. Esta investigação vem contribuir para a necessidade de estudar a pirataria digital entre culturas, colaborando para um melhor entendimento de como a intenção de piratear é afetada e como os decisores devem ajustar as políticas entre países. A maioria dos fatores analisados não são novos nesta linha de investigação, contudo a exceção é o fator perceived value. Outra inovação é o desenvolvimento de dois modelos: um que engloba toda a amostra (modelo geral) e outro apenas os indivíduos que já piratearam no passado (modelo pirata). Esta separação permite ainda analisar o impacto do factor past piracy behavior na intenção de piratear e comparar resultados entre modelos. Os dados necessários foram recolhidos junto de estudantes e examinados utilizando a análise de equações estruturais. Os resultados mostraram a existência de diferenças significativas entre os modelos, no entanto alguns fatores apresentaram um efeito significante sobre a intenção em ambos, nomeadamente os fatores perceived behavioral control e moral obligation. Todavia o fator subjective norms apenas apresentou um efeito significativo no modelo geral. O fator punishment certainty teve em ambos os modelos um efeito significativo sobre o fator perceived behavioral control. De entre os que não apresentaram um efeito significativo em ambos os modelos encontra-se o fator attitude. Porém, os seus antecedentes demonstraram alguns resultados distintos entre os modelos. Os fatores punishment certainty e severity não revelaram um efeito significativo em ambos os modelos, contudo os fatores digital media cost e perceived value demostraram um efeito significativo sobre a attitude, mas apenas no modelo que considera a amostra completa. O modelo pirata confirmou ainda a existência de um efeito significativo e forte do comportamento passado na intenção futura de piratear. Por fim os resultados são discutidos e são propostas formas de intervenção. v Table of Contents 1. Introduction.......................................................................................................................... 1 2. Literature Review................................................................................................................. 6 2.1. Piracy Research ........................................................................................................ 6 2.1.1. Early Piracy Research ............................................................................................. 6 2.1.2. Digital Piracy Research ......................................................................................... 11 2.1.3. Summary .............................................................................................................. 13 2.2. Model Development: Theoretical Foundations and Hypotheses ........................... 14 2.2.1. Theory of Planned Behavior .................................................................................. 14 2.2.2. Moral Obligation................................................................................................... 17 2.2.3. Past Piracy Behavior ............................................................................................. 18 2.2.4. Deterrence Theory ................................................................................................ 19 2.2.5. Software and Media Cost ...................................................................................... 21 2.2.6. Perceived Value .................................................................................................... 22 2.2.7. Conceptual Model ................................................................................................. 23 3. Research Methodology ....................................................................................................... 25 3.1. Questionnaire .......................................................................................................... 25 3.1.1. Measured Factors and Correspondent Sources ....................................................... 26 3.2. Data ......................................................................................................................... 27 3.3. Estimation Procedure ............................................................................................. 28 4. Results ................................................................................................................................ 30 4.1. First Exploratory Results ....................................................................................... 30 4.2. Multivariate Analysis .............................................................................................. 31 4.2.1. Full Model ............................................................................................................ 33 4.2.1.1. Measurement Model ............................................................................................. 33 4.2.1.2. SR Model.............................................................................................................. 38 4.2.2. Pirate Model ......................................................................................................... 41 4.2.2.1. Measurement Model ............................................................................................. 41 4.2.2.2. SR Model.............................................................................................................. 43 5. Discussion and Implications ............................................................................................... 45 5.1. TPB Variables, Moral Obligation and Past Piracy Behavior ................................ 45 5.2. Punishment Certainty and Severity ....................................................................... 47 vi 5.3. Digital Media Cost and Perceived Value ................................................................ 48 6. Limitations and Future Research ...................................................................................... 50 7. Conclusion .......................................................................................................................... 51 8. References........................................................................................................................... 53 9. Appendix ............................................................................................................................ 60 vii List of Tables Table 1: Piracy behavior research ..................................................................................... 13 Table 2: Questionnaire instrument scale factors ................................................................ 26 Table 3. Sample demographics ......................................................................................... 31 Table 4. Exploratory factor analysis ................................................................................. 32 Table 5. Consistency statistics .......................................................................................... 36 Table 6. Squared correlation between factors ................................................................... 37 Table 7. Model results summary ...................................................................................... 49 viii List of Figures Figure 1: Conceptual Model.. ........................................................................................... 24 Figure 2. Final CFA Full Model . ..................................................................................... 35 Figure 3. Final Full Sample SR Model. ............................................................................ 39 Figure 4. Final CFA Pirate Model .................................................................................... 42 Figure 5. Final Pirate SR Model. ...................................................................................... 44 5 4. The results discussion and implications are in Section 5. Limitations and future research directions are in Section 6 and Section 7 concludes. A better understanding of consumer’s behavior will help develop new strategies and ideally reduce piracy. 6 2. Literature Review Digital piracy is not a new subject. It has been around us for quite some time now. As is indispensable, this section takes on previous research and shed a light on the literature that has been produced so far. It starts by looking to specific areas of piracy, like software, music, movies and culminates on digital piracy. While in this first part the focus is on results, employed variables and theories (all in a brief manner), the second part emerges from the first one, which helped select the research constructs that will be used. This second part is where the model development starts, the theoretical foundations are lay down and hypothesis are developed. It is indeed a more specific and in-depth analysis, that truly dictates this investigation path. 2.1. Piracy Research 2.1.1. Early Piracy Research The first major concern regarding copyright infringement was software piracy. Researchers have been investigating this phenomenon since the late 1980s, but the first studies were mostly descriptive surveys (Peace et al., 2003; Limayem et al., 2004). One of the first empirical works examining software piracy, using a model based on a theoretical framework was Christensen and Eining (1991), applying the Theory of Reasoned Action 3 (TRA) (Fishbein and Ajzen, 1975). They found that attitudes toward piracy and subjective norms were both related with the student’s propensity to pirate. Their investigation indicated as well that this kind of behavior was not seen as inappropriate and that individual’s believed that others shared the same view. Gopal and Sanders (1997) investigated the impact of deterrent and preventive measures on software developer’s profits, and found that preventive controls may have a negative impact on profits, but on the other hand deterrent strategies can potentially increase them. They also found that deterrence measures, ethics, sex and age are related to 3 The TRA exposes human behavior as function of attitude toward the behavior and social norms. Further explanation is provided in the second part of this section. 7 an individual’s propensity to pirate. In a posterior study the authors concluded that the size of a software industry is positively related to the government propensity to be an active force in the fight against piracy, and that it is inversely related to piracy rates (regardless of a country wealth) (Gopal and Sanders, 1998). Consequently the existence of domestic software industry may be a determinant factor against piracy. Later on Gopal and Sanders (2000) established the existence of a significant effect between income and global piracy rates, and they proposed global price discrimination as the first line of defense against piracy. Shin et al. (2004), analyzed software piracy rates for 49 countries, considering per capita GDP and national collectivism as independent variables. Finding evidence of a negative correlation between per capita GDP and the software piracy of a country; on the opposite the relationship is positive with a country’s collectivism. This supports Gopal and Sanders (2000) results and implies that not only “poor countries are more involved in software piracy, but also that high collectivistic countries are involved in piracy” (Shin et al., 2004, p.105). Tan (2002) focused his attention on the ethical judgment associated with software piracy, constructing a research framework that incorporated several behavioral theories and moderating variables capable of influencing ethical decision-making. His model 4 considered the effect of moral intensity, perceived risk and moral judgment, taking also in account the influence of some moderating variables 5 . Results supported the hypothesis that both perceived risks and moral judgment have a negative impact on intention, in other words, the higher the perceived risk/moral judgment of consumers, the lower will be their intention to pirate. Peace et al. (2003) proposed a framework based on the theory of planed behavior 6 (TPB), complemented with the expected utility theory and deterrence theory 7 . Using central 4 The estimation method used was the two-step hierarchical regression analysis. 5 Price, gender, age, educational attainment, income and past purchase experience. 6 The TPB exposes human behavior as function of attitude toward the behavior, subjective norms and perceived behavioral control. Further explanation is provided in the second part of this section. 7 This theory proposes that as punishment probability and punishment level are increased, the level of illegal behavior should decrease (Peace et. al., 2003). 8 factors identified by these theories, Peace et al. (2003) proposed a model to evaluate the impact on software piracy done by individuals in their workplace. Each factor identified by the expected utility theory and deterrence theory (punishment severity, punishment certainty and software cost) was included as an antecedent to the attitude factor. Punishment certainty was also considered as an antecedent of perceived behavior control. Their model was tested using a structural equation modeling 8 (SEM) technique called partial least squares (PLS) path modeling 9 and accounted for 65 percent of the variance (𝑅2) in software piracy intention. It showed that attitude, subjective norms, and perceived behavior control significantly influence people’s intention. Attitude presented the strongest effect on piracy intention, and its predicted antecedents were found to have a strong relationship with attitude, also the hypothesis of punishment certainty as a control belief for perceived behavior control was strongly supported. Similar results were found by d’Astous et al. (2005) for online music piracy, with all the factors derived from the TPB having a positive and statistically significant impact on the intention to engage in piracy; additionally past piracy behavior also had a strong influence on intention. Analyzing factors that affect software piracy intentions and its subsequent result on behavior, Limayem et al. (2004) constructed a model 10 based on Triandis’ behavioral model (Triandis, 1979, cf. Limayem et al. , 2004), and found that social factors, along with perceived consequences had a positive relationship with intention to pirate software, and that habits and facilitating conditions affect the actual software piracy behavior. Surprisingly intentions did not led to engagement. A possible explanation is that intention is being override by habits and facilitating conditions. Their model analyzed using PLS only explained 17 percent of the variance in piracy, with the authors defending that further research is need before starting questioning previous research. Similar to Limayem et al. (2004), Phau et al. (2014) proposed not only to identify factors capable of influencing intention, but also the actual engagement in digital movies 8 A detailed explanation of SEM is provided on section 3. 9 Also referred to as PLS-SEM. 10 Their model relied on following factors to explain the behavioral process: habit, affect, perceived consequences/beliefs, social factors, facilitating conditions and intention. 9 piracy 11 , using the TPB. The theory was used in an unusual way, since attitude toward behavior were measured by one’s attitude towards digital piracy of movies and their moral judgment. Subjective norms were measured by social habit and perceived behavior control by self-efficacy. Their data was analyzed using SEM 12 and they found that from the TPB original determinants only attitude towards digital piracy of movies presented an unexpected result, having a negative impact on intention. According to the authors this may be attitude being override by the positive influence coming from moral judgment. Moral judgment, as expected, had a negative impact on intention (supporting Tan (2002)) and engagement. Contrarily social habit positively influenced individuals to pirate. At last, but not least important, Phau’s et al. (2014) research showed a positive (but weak) relation between intention and the actual act of pirating digital movies. This result clearly shows the need to further study the relation between intention and engagement, at least in digital piracy. Since previous authors (e.g. Ajzen (1991)) found intention as an accurate predictor of behavior itself. Another theory that has been used to explain human behavior and software piracy in particular is the equity theory 13 . Douglas et al. (2007) using reciprocal fairness, procedural fairness 14 and distributive fairness 15 as antecedents of equity found that the first two factors were significant determinants, and that equity had a negative and statistically significant impact on software piracy, in other words, the higher the perceived fairness/justice of the exchange by the consumer, the lower software piracy will be. While many previous studies have focused on software piracy, others have dedicated their attention to study different formats of digital piracy, such as music, movies, 11 An important limitation presented by this research is that, the actual behavior was measure through a proxy. 12 However they fail to specify the SEM technique used, appearing to be a covariance-based SEM. 13 Equity theory addresses human pursuit of fairness and justice in a social exchange (Douglas et al., 2007). 14 Procedural fairness “is represented by the involvement and interaction of the producer with the consumer” (Douglas et al., 2007, p. 505). 15 Distributive fairness “relates to purchase of software by different groups of consumers” (Douglas et al., 2007, p. 505), for example price discimination strategies between consumers. 10 video games, and other digital media. An important characteristic of digital goods is that they “have high initial production costs, and very low - approaching zero - reproduction costs. They also have characteristics of a public good in that sharing with others does not reduce a consumer’s utility for the product” (Bhattacharjee et al., 2003, p. 108). These properties facilitated the widespread of pirated content worldwide. Bhattachrjee et al. (2003) suggested that music piracy shows a number of similarities with software piracy. According to this author, despite the significant price difference between software and music albums, it is reasonable to admit that demand is quite elastic for both, since increasing the price of digital material has a strong positive effect on piracy. Furthermore, with increasingly higher internet connections consumer’s price sensitivity increases. Gopal et al. (2004) sought out to have a better understanding of the behavior dynamics that drive individuals to pirate digital audio files, using the concept of piracy club size 16 as a proxy of piracy level. They found that ethics has a very strong relationship with club size (ethical individual’s will be less likely to share pirated files), and that justice is positively related to ethics, but having a very small effect on club size. In addition, the amount of money saved by using pirated content was a moderately strong predictor of piracy. The author concludes that the high price of a proper licensed audio CD is an incentive to piracy, indicating that users are extremely price-sensitive when presented with the possibility of illegally download an audio file. However, income did not influence the club size. Their results are consistent with Bhattachrjee et al. (2003) that found that income has a negative effect on piracy but only for unknown songs, when the choice is made relatively to a known music, income doesn’t affect the decision. Furthermore they indicate that the general ethical model of software piracy is broadly applicable to digital audio piracy. 16 Individuals with similar beliefs join together to share unlicensed material, benefitting from sharing the costs incurred when buying the proper licensed material at market price, which is then distribution for all the club members. 11 2.1.2. Digital Piracy Research More recently Al-Rafee and Cronan (2006), while examining factors that influence an individual’s attitude toward pirating digital material, found that subjective norms (influence of significant others), cognitive beliefs about the outcome of behavior, perceived importance of the issue, machiavellianism, age, happiness and excitement were all significant predictors of attitude. Moral judgment, distress, and sex were not significant variables influencing attitude. Their investigation was supported on the construct that attitude is the most significant factor influencing behavioral intention (e.g. Trafimow and Krystina, 1996; Peace et al., 2003); therefore attitude toward digital piracy was treated as a dependent variable. According to them, understanding these factors is important because attitude can be changed through persuasion and other means, making it possible to influence behavior (in an indirect fashion). Thus, a better understanding of these factors could be essential in lowering piracy. This study also supports previous research by showing that consumers believe that digital material is overpriced and that they will not be caught. In 2008, the same authors (Cronan and Al-Rafee, 2008), using a student sample from a business college, sought to analyze factors that influence an individual’s intention to pirate software and media, attempting to offer a better understanding of digital piracy behavior. Antecedents to digital piracy behavior were investigated using an extended TPB model, which included moral obligation and past piracy behavior in addition to the original TPB determinants. It was (separately) hypothesized that individuals with higher attitude, subjective norms, perceived behavioral control and past piracy occurrences will correspond with a greater intention to pirate; on the contrary, higher moral obligations correspond with a lower intention. The result of the SEM analysis indicated that their model explained 70.8 percent of the variance in digital piracy intention, with only subjective norms not being a significant predictor of intention. Al-Rafee and Dashti (2012) also argue that individual’s intention regarding digital piracy could change between cultures. Using two samples from different cultures (United States and Middle East) they developed a model expanding the TPB framework with moral 12 obligation. The model (analyzed using PLS) presented substantial explanatory power in both cultures, with only the variable subjective norms in the U.S. not being a significant predictor of intention. As expected the variables had a different impact on people’s intention: in the U.S. sample, intention was strongly affected by their ability to pirate and moral obligation, where in the Middle East sample one’s attitude was the foremost important factor, followed by the ability to pirate. Their work shows that culture may have a significant impact in intention, and subsequently in individuals’ behavior when it comes to pirate digital media. It also highlights the need to study digital piracy across different cultures, since policies should be adjusted (fine tuning) to each country by governments and copyright organizations. This research will examine digital piracy using the TPB as framework, since it shown itself as a reliable model to investigate behavioral intentions associated with digital piracy. However TPB will be extend using the expected utility theory, the deterrence theory, as well as other proven behavioral constructs like ethics and past behavior. People still may ask why investigate digital piracy as a whole. The answer is that it’s reasonable to assume that any individual capable of download a music file is capable of download any other type of file. Although some might say that downloading software and video games is only half of the job, because the next step is to install them, it is also true that most uploaders include tutorials that teach how to install the illegal material. Thus this additional barrier is easily overtaken. It is also very common for an individual to find in the same website links/torrents to download music, movies, software and other digital material. Finally, storage capabilities and internet connection speed, barriers pointed in the past as deterrents to piracy, are no longer a problem (at least in developed countries). Even more the storage barrier is now totally obliterated since the streaming of unlicensed material like music, movies and TV shows is becoming commonly used. 13 2.1.3. Summary The following table synthesizes the presented piracy research. Table 1: Piracy behavior research (source: author) Ethical Issue Researcher Factors Influencing Intention or Piracy Theoretical Underpinning Methodology Main Results Software Piracy Christensen and Eining (1991) Attitude Subjective norms Teory of reasoned action Chi-square statistics Multiple regression analysis Attitude toward piracy and subjective norms were directly related to software pircy. Software Piracy Tan (2002) Moral intensity Perceived risk Moral judgment Rest’s fourcomponent model Jones’ issuecontingent model Two-step hierarchical regression analysis Perceived risks and moral judgment had a negative impact on intention. Software Piracy Peace et al. (2003) Attitude Subjective norms Perceived behavioral control Punishment severity Punishment certainty Software cost Teory of reasoned action Theory of planned behavior Expected utility theory Deterrence theory PLS-SEM 𝑅2= 0.65; TPB components presented a positive impact on intention. All the anticipated hypotheses were supported. Software Piracy Limayem et al. (2004) Habit Affect Perceived consequences/beliefs Social factors Facilitating conditions Intention Triandis’ behavioral model PLS-SEM Social factors and perceived consequences had a impact on intention. Habit and facilitating conditions had a impact on the actual behavior. Software Piracy Douglas et al. (2007) Reciprocal fairness Procedural fairness Distributive fairness Equity Equity theory Covariance SEM Equity had a negative and statistically significant impact on piracy. Music Piracy Gopal et al. (2004) Age Gender Ethical Index Justice Money Saved Expected utility theory Deterrence theory Covariance SEM Club size is positively influenced by gender and money saved, while negatively influenced by the remaining factors. Music Piracy d’Astous et al. (2005) Attitude Subjective norms Perceived behavioral control Past behavior Personal consequences Ethical predispositions Theory of planned behavior Multiple regression analysis Test of mediation procedure TPB components presented a positive impact on intention. Personal consequences and ethical predispositions presented a negative relationship with attitude. Past behavior showed a positive relationship with attitude and intention. 14 Ethical Issue Researcher Factors Influencing Intention or Piracy Theoretical Underpinning Methodology Main Results Movies Piracy Phau et al. (2014) Affect Attitude Moral judgment Social habit Self-efficacy Intention Theory of planned behavior Neutralisation theory SEM Attitude and moral judgment had a negative impact on intention. Moral judgment also had a negative impact on engagement. The actual act of pirating was positively influenced by social habit and intention. Digital Piracy Al-Rafee and Cronan (2006) Subjective norms Cognitive beliefs Perceived importance Machiavellianism Age Happiness and excitement Moral judgment Distress Gender Theory of planned behavior Stepwise regression analysis 𝑅2= 0.436 Only moral judgment, distress, and sex were not significant variables influencing attitude. Digital Piracy Cronan and Al-Rafee (2008) Attitude Subjective norms Perceived behavioral control Moral obligation Past piracy behavior Teory of reasoned action Theory of planned behavior SEM R2= 0.708 Only subjective norms were not a significant predictor of intention. Digital Piracy Al-Rafee and Dashti (2012) Attitude Subjective norms Perceived behavioral control Moral obligation Teory of reasoned action Theory of planned behavior PLS-SEM Only subjective norms in the U.S. sample were not a significant predictor of intention. 2.2. Model Development: Theoretical Foundations and Hypotheses 2.2.1. Theory of Planned Behavior The theory of planned behavior (Ajzen, 1985, 1991, 2002a) is a well known, recognized and empirically supported theory for predicting intentions and behavior (Armitage and Conner, 2001). The theory emerged from the theory of reasoned action (Fishbein and Ajzen, 1975), which was designed to predict behaviors that are under volitional control, this is, behaviors that a person can decide at will to perform. However, it is clear that most of the behaviors are not under volitional control, internal factors (e.g. information, skills, abilities, power of will) and external factors (e.g. lack of time and opportunity) can compromise intention and ultimately the behavior. In response to this 21 2.2.5. Software and Media Cost It appears that economic incentives play a major role in consumer’s behavior decision, with software and media price being a determinant factor. Although other economic factors like income, money saved and perceived cost-benefit are not the target of this investigation, they were used by previous researchers and represent an important insight to a consumer’s decision process. Software piracy rate was found to have a significant negative correlation with per capita GDP (and per capita GNP) mainly in poor countries, with investigators finding an inflection point at USD 6000, where income level below the inflection point reveal a stronger negative relation (Gopal and Sanders, 2000; Shin et al., 2004). According to Gopal and Sanders (2000) this reveals an important problem: people with low income cannot afford high software prices, thus piracy is influenced by the significant price differential between legal and pirated content 17 . They propose address this problem through global price discrimination, which according to them is capable of maximize developer’s profits 18 and create incentives to government action (e.g. enforcement of copyright laws). Peace et al. (2003) also found evidence supporting this type of strategies, with software cost having a strong positive relationship with one’s attitude toward piracy. It is then expected that software price will have an important role in the decisionmaking process, since software packages usually are the most expensive digital goods, but surprisingly in music, price is also an important factor. The higher the price, the stronger is the positive effect on piracy, pointing to a quite elastic demand, as in software (Bhattacharjee el al., 2003; Gopal et al., 2004). In the motion picture industry, consumer’s perceived cost-benefit has a positive impact on intention to buy pirated content, indicating as well that reducing the prices of movie DVDs would most likely have a negative impact on piracy (Wang, 2005). 17 At the time was usual to buy physical pirated content (e.g. CDs) as opposed to downloading. These direct costs are the one’s referred by the authors, however today pirates usually download all their unlicensed content from the internet, incurring only in indirect costs, as having a PC with internet connection (we believe these are indirect costs because the ordinary person will not primarily use their PC to pirate digital material). 18 If developers make their software more affordable, it’s expected that more people will buy it. 22 In a general way, consumers seem to believe that digital media is overpriced, using piracy as a mean to save money (AI-Rafee and Cronan, 2006). So it appears that even when the price of a digital good is low, and probably does not represent an economic burden, it still has an impact on the decision-making process. If piracy behavior is modeled through the expected utility from choosing between illegal download, purchase, or do without the digital good, a rational agent will choose the utility function 19 that maximizes his expected utility. Therefore utility is used as a way to describe his preferences among the alternatives, and the correspondent characteristics of each alternative (Varian, 2009). Considering the expected costs and benefits, he will select the alternative that he believes is associated with the most desirable outcome. If piracy yields a positive surplus, despite being negatively affected by the risk inherent to punishment certainty and severity (among other factors), a lower price would decrease the payoff, ceteris paribus. The cost of digital material can be incorporated into the TPB as an antecedent of attitude by the same reasons appointed in the deterrence theory. It is therefore expected that a higher the financial cost will correspond to a higher attitude towards piracy, due to the higher expected payoff. As such, can be hypothesized that: H9: Digital media cost will have a positive influence on attitude toward pirate digital materials. 2.2.6. Perceived Value It is expected that the higher the price the higher will be the attitude towards piracy, however the perceived price may not be enough to evaluate a digital good, and in this way another factor was added to capture a broader set of perceived characteristics. This factor is perceived value, and helps us understand if consumers perceive digital goods as high value products, that are worthy of their financial cost, or on the other end, the 19 We may look at this function as an ordinal utility function, however the utility function only exists if a consumer preferences respect the following axioms: completeness, reflexivity, transitivity and continuity. The first tree axioms render the behavior of a rational agent. 23 time, effort and risk associated with pirate them. So what is value? When someone is evaluating the value of a certain good, they are forming their own construct, thus perceived value is an abstract concept that is highly personal and individualistic (Zeithaml, 1998;Chu and Lu, 2007). Zeithaml (1998, p.14) defined it as the “consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given”. Therefore, if a consumer believes that a product has a low (or high value), it is the net result between the assessed gains (e.g. intrinsic attributes, volume, quality) and sacrifices (e.g. money, time, effort). Previous authors have studied perceived value in very diverse products or services, and found evidence of a positive relation between perceived value and consumer willingness-to-buy (or purchase intentions) (Dodds et al.1991, Chu and Lu, 2007). However, no one ever (at least as far as we know) applied this concept to digital piracy and so we may expect that the higher the perceived value, the lower will be one’s attitude to pirate. Therefore, it is hypothesized that: H10: Perceived value will have a negative influence on attitude toward pirate digital materials. It is also expected that price will have an influence on perceived value however, the direction (positive or negative) of that influence is uncertain. Dodds et al. (1991) told us that price has a double function, it may serve has an indicator of sacrifice, leading to a negative impact on the perceived value, and at the same time can be an indicator of quality, since higher prices lead to higher perceived quality and as a result to a higher perceived value. This tradeoff forms an individual’s perception of value, with the authors finding some mixed results for the relation price-quality, but support for a negative relation between price and a buyer’s perception of value, as the price increases the perceived value decreases. 2.2.7. Conceptual Model An easy and simple way to summarize all the postulated hypotheses is to observe the conceptual model presented in the next page. This conceptual model truly represents 24 not one, but two models: a first one will consider the full sample, but not evaluating the effect of past piracy behavior in intention (Full Model); and a second one, that has been obtained by adding past piracy behavior and, as consequence, will only considers those who had pirated (Pirate Model). The dashed path between past piracy behavior and intention is meant to indicate exactly this, since this factor will only be in one of the models. Figure 1: Conceptual Model. Expanded from Peace et al. (2003) and Cronan and Al-Rafee (2007). 25 3. Research Methodology After a review of piracy research and the structural theories on which this work is built, it is now time to bring the conceptual model out of the paper. This section makes this required next step, to put it simply, answers to the following three questions: how was the questionnaire developed; how the latent variables (factors) will be measure using primary data; and how the collected data will be analyzed. This section is divided in three parts. In first one is covered the development of the questionnaire, being highlighted the measured factors as well the corresponding sources. The second part addresses data collection and the last one explains a technique called structural equation modeling. This technique will be used to validate the data and elaborate the models thus, an in depth look to SEM is indispensable. 3.1. Questionnaire The data used in this research was collected using a questionnaire written in Portuguese, which can be found on Appendix A. It was also created an online version 20 to facilitate distribution and reach as many people as possible. In order to avoid misinterpretations the initial page explained what digital piracy is, and how the questionnaire should be filled. Individuals were asked to voluntarily participate, their anonymity and confidentiality being assured by the author. These aspects had to be assured because digital piracy is an illegal act and this research has a strong ethical component, as such these measures may help to facilitate responses but also, and more importantly, truthful ones. These concerns were also very important in the decision of not measuring behavior itself, but using instead intention as a proxy for their predicted digital piracy behavior, since it would be impossible to identify the respondents to a follow-up questionnaire. 20 The online version was identical to the paper version, but with some visual modifications to better accommodate it to the online platform 26 To help ensure measurement reliability and validity, all the factors and measurement variables used were based on previous validated research, as we can see on Table 2, but some adjustments were necessary to conform the indicators to this research. A preliminary version of the questionnaire was developed and pre-tested in one focus group discussion 21 , as well distributed to individuals that gave their feedback. This was a necessary and very important step to ensure that respondents understand all the questions. Overall, the feedback was positive, with some punctuation and words/sentences changed due to their ambiguous statement. The instructions to fill in the questionnaire also emerged from the pre-test, considering that those who were not familiar with Likert scales did not understand immediately what was being asked. 3.1.1. Measured Factors and Correspondent Sources Following the hypotheses developed and the theoretical foundations on that they are constructed, it is time to specify how the unobserved variables presented on the conceptual model (Figure 1) will be measured. All the factors and correspondent indicators that will be used are listed in Table 1, with all the items being scored on a seven-point Likert scale, ranging from “strongly agree” to “strongly disagree” in almost all indicators. Table 2: Questionnaire instrument scale factors Factor Source No. of indicators Indicator location on questionnaire Intention (INT) Cronan and Al-Rafee (2008); Peace et al. (2003) 3 Page 3; Set 1 Attitude (ATT) Cronan and Al-Rafee (2008) 4 Page 2; Set 1 Subjective Norms (SN) Cronan and Al-Rafee (2008) 3 Page 3; Set 2 Perceived Behavioral Control (PBC) Cronan and Al-Rafee (2008) 5 Page 2; Set 2 Moral Obligation (MO) Cronan and Al-Rafee (2008) 3 Page 5; Set 1 21 The focus group discussion took place in early March, where four students colleagues participated. 27 Factor Source No. of indicators Indicator location on questionnaire Past Piracy Behavior (PPB) Cronan and Al-Rafee (2008); Author 2 Page 2; Set 3 Punishment Severity (PS) Peace et al. (2003) 2 Page 3; Set 3 Punishment Certainty (PC) Peace et al. (2003) 2 Page 4; Set 1 Digital Media Cost (DMC) Peace et al. (2003) 3 Page 4; Set 2 Perceived Value (PV) Dodds et al. (1991) 3 Page 4; Set 3 Note: The questionnaire can be found on Appendix A 3.2. Data Data was collected using an online questionnaire and a paper one. This decision may have a biasing effect on the results, however, it should be minor and negligible. The URL to the online questionnaire was sent by e-mail to 28 715 students of University of Porto, while the paper one was administered to 79 students during regular class time in Carrazeda de Ansiães high school. The questionnaire was online during the month of April and the paper version was also distributed in the middle of the same month. A total of 590 questionnaires were collected. From these, twenty-seven had missing data which led to a sample of 563 questionnaires with complete data. The use of a student sample was deemed appropriated in the context of this research for four main reasons: a) Previous researchers have shown that digital piracy is generalized among the students (Im and Van Epps, 1991; Cronan and Al-Rafee, 2008); b) Students samples have been used in several piracy studies (Peace et al., 2003; Gopal et al., 2004; Limayem et al., 2004; D'Astous et al., 2005; Wang, 2005; AI-Rafee and Cronan, 2006; Lysonski and Durvasula, 2008; Cronan and Al-Rafee, 2008; Al-Rafee and Dashti, 28 2012; Phau et al., 2014), thus using a student sample will facilitate comparisons between studies; c) Today’s students will be tomorrow’s work force; and d) Since it is difficult to use random sampling methodologies due to the scope of the work, students constitute a good target population for convenience sampling. 3.3. Estimation Procedure Structural Equation Modeling (SEM) was used in this investigation. SEM is a technique to “specify, estimate, and evaluate models of linear relationships among a set of observed variables in terms of a generally smaller number of unobserved variables” (Shah and Goldstein, 2006, p.149). However we should not look to SEM as a technique, but instead as set of related procedures design to evaluate how well a proposed conceptual model is consistent (fits) with the data (Kline, 2011). Furthermore, SEM allows multiple exogenous and endogenous variables to be estimated simultaneously (Anderson and Gerbing, 1988), this represents a major advantage over multiple regression. Why use SEM? SEM has been considered a better (and best suited) technique for theory testing and development than estimation methods that analyze a single equation at a time, because (when all the prerequisites are fulfilled) the estimation methods employed by SEM provide a more efficient and consistent parameter estimates; it also deals with the overall model fit (Anderson and Gerbing, 1988; Kline, 2011). This technique is also commonly used in piracy research, for example, as we saw was used by Peace et al. (2003), Limayem et al. (2004), Douglas et al. (2007), Cronan and Al-Rafee (2008), and Al-Rafee and Dashti (2012). Observed variables are usually used as an indirect measure of unobserved variables, and are typically referred to as an indicator (or measurement variable), while unobserved variables are normally called latent variables (factors or research constructs), and generally correspond to hypothetical constructs or factors (Gefen et al., 2000; Kline, 2011). 29 A SEM model combines a measurement model and a structural model. The measurement model (a confirmatory factor analysis model) is an a priori model (developed from theoretical expectations) that identifies the latent variables and their correspondent indicators (Gefen et al., 2000; Kline, 2011). The structural model (a path model) represents the hypothesized effect priorities, however dissimilar from path models these effects can, and usually involve latent variables (Gefen et al., 2000; Kline, 2011). The most common SEM model, that will be used in this work, is a structural regression model (SR model), also known as LISREL model. This is considered a covariance-based SEM (CB-SEM) (Anderson and Gerbing, 1988; Gefen et al., 2000), where “model fitting to compare the covariance structure 22 fit of the researcher’s model to a best possible fit covariance structure” is used (Gefen et al., 2000, p. 26). The fit between the data and the conceptual model is assessed through a series of model fit tests, as the ratio of chi-square to degrees of freedom, the goodness of fit index (GFI), the adjusted goodness of fit index (AGFI), and the root mean residual (RMR). On the other hand (at the individual path level) construct validity and reliability are assessed using confirmatory factor analysis 23 (CFA) (see Gefen et al., 2000; Kline, 2011; Marôco, 2014). The default method of estimation in SR models is the maximum likelihood (ML) estimation, where “estimates are the ones that maximize the likelihood (the continuous generation) that the data (the observed covariances) were drawn from this population” (Kline, 2011, p.154). It is assumed that variables are continuous and normally distributed. However, as this assumption is frequently relaxed, the variables will be measured using a Likert scale. Therefore, it will be assumed (as Marôco, 2014) that as long as the number of categories or scale points used is high (at least five) and that the distribution is close to a normal distribution they can be treated as continuous variables. 22 Covariance structure is the part of a SEM that represents hypotheses about variances and covariances (Kline, 2011). 23 The CFA should show convergent validity and discriminant validity, otherwise the measurement model must be respecified. 30 4. Results As the title above suggests, it is now time to focus on results. This section is divided in two parts, starting with a descriptive analysis. This type of analysis despite its simplicity is very important, allowing for a sample overview. First we take a look at sample demographics and then past piracy behavior. Past behavior was also segmented according to demographic characteristics. The second part is where the multivariate analysis begins. An exploratory factor analysis is carried out (to have a deeper view at the data) followed by SEM. To validate the measurement models a confirmatory factor analysis is implemented. Given an acceptable measurement model, the second step is to identify and specify the structural model. The final SR models (structural model + measurement model) are then presented and evaluated. All the results were obtained using SPSS Statistics 21 (essentially for descriptive data analysis) and subsequently AMOS 21 (for SEM). 4.1. First Exploratory Results A first descriptive analysis shows that more than half were female students and 37.8% (213 students) were male, the average age was 23 years. The majority of the students (83.3%) were either bachelor or master students, and with 79.9% of the students revealing that, they do not do anything else besides studying. About 75% of the students reported having pirated previously, from these 40.4% disclosed that they do pirate a lot, and 25.4% does it in a daily base or almost daily. Table 3 gathers all the presented information and offers a more detailed view. The data also shows that almost 81% of the men admitted to pirate, while this number was lower for the women, but still very high (71.1%). Another interesting way to look at piracy past behavior is to break it down by education level. Only 9.6% of the high school students admitted that they never had pirated, which represents the lowest value of all, as for the reaming (Doctoral, Master’s and Bachelor’s students) they all presented similar values, between 25% and 27.6%. 37 Construct validity is used to assess if the used variable truly measure/represents the construct that we want to evaluate (O’Leary-Kell and Vokurka, 1998; Marôco, 2014). Since factor validity was already examined remains to establish convergent and discriminant validity. The first one occurs when indicators load significantly on their corresponding factors, this means that the behavior of an indicator is essentially explained by its correspondent factor, the last one is a measure of how unique each set of indicators is, thus discriminant validity assess the correlations between the factors (Marôco, 2014). Convergent and discriminant validity were analyzed using the average variance extracted (AVE) for each construct, as described in Fornell and Larcker (1981). According to Hair et al. (1998) an AVE ≥ 0.5 is an adequate indicator of convergent validity, and as we can see on Table 5 all constructs presented and suitable AVE. On the other end, we fulfill the required condition for discriminant validity when the squared correlation between two factors is equal or lower than the individual AVE for them (Fornell and Larcker, 1981). Comparing the average variance extracted per factor with the correspondent squared correlation values on Table 6 we can see that the previous condition is accomplished. Table 6. Squared correlation between factors Factors Squared Correlation Intention (INT) and Attitude (ATT) 0,309 Intention (INT) and Perceived Behavioral Control(PBC) 0,452 Intention (INT) and Moral Obligation (MO) 0,484 Intention (INT) and Subjective Norms (SN) 0,490 Intention (INT) and Perceived Value (PV) 0,005 Intention (INT) and Digital Media Cost (DMC) 0,036 Intention (INT) and Punishment Severity (PS) 0,062 Intention (INT) and Punishment Certainty (PC) 0,158 Attitude (ATT) and Perceived Behavioral Control(PBC) 0,172 Attitude (ATT) and Moral Obligation (MO) 0,448 Attitude (ATT) and Subjective Norms (SN) 0,259 Attitude (ATT) and Perceived Value (PV) 0,000 Attitude (ATT) and Digital Media Cost (DMC) 0,036 Attitude (ATT) and Punishment Severity (PS) 0,020 Attitude (ATT) and Punishment Certainty (PC) 0,065 Perceived Behavioral Control(PBC) and Moral Obligation (MO) 0,266 38 Factors Squared Correlation Perceived Behavioral Control(PBC) and Subjective Norms (SN) 0,398 Perceived Behavioral Control(PBC) and Perceived Value (PV) 0,023 Perceived Behavioral Control(PBC) and Digital Media Cost (DMC) 0,026 Perceived Behavioral Control(PBC) and Punishment Severity (PS) 0,040 Perceived Behavioral Control(PBC) and Punishment Certainty (PC) 0,184 Moral Obligation (MO) and Subjective Norms (SN) 0,423 Moral Obligation (MO) and Perceived Value (PV) 0,012 Moral Obligation (MO) and Digital Media Cost (DMC) 0,014 Moral Obligation (MO) and Punishment Severity (PS) 0,058 Moral Obligation (MO) and Punishment Certainty (PC) 0,141 Subjective Norms (SN) and Perceived Value (PV) 0,022 Subjective Norms (SN) and Digital Media Cost (DMC) 0,041 Subjective Norms (SN) and Punishment Severity (PS) 0,115 Subjective Norms (SN) and Punishment Certainty (PC) 0,270 Perceived Value (PV) and Digital Media Cost (DMC) 0,196 Perceived Value (PV) and Punishment Severity (PS) 0,002 Perceived Value (PV) and Punishment Certainty (PC) 0,006 Digital Media Cost (DMC) and Punishment Severity (PS) 0,000 Digital Media Cost (DMC) and Punishment Certainty (PC) 0,017 Punishment Severity (PS) and Punishment Certainty (PC) 0,231 4.2.1.2. SR Model Given an acceptable measurement model, the second step is to identify and specify the structural model, this type of strategy (two-step) helps ensure that the measurement model is correctly validated (Marôco, 2014). In this section is introduced the final adjusted (using the modification indices) SR model (structural model + measurement model) and evaluated its adjustment quality using a set of fit statistics. 39 Figure 3. Final Full Sample SR Model. Path coefficient estimates are reported as standardized (∗∗p<0.01; ∗0.01≤p≤0.05; ns (not significant)p>0.05). 40 The overall model fit is satisfactory (𝑋2𝑑𝑓 ⁄= 2.14; 𝐶𝐹𝐼 = 0.970; 𝐺𝐹𝐼 = 0.934;𝑅𝑀𝑆𝐸𝐴 = 0.046;𝑃[𝑟𝑚𝑠𝑒𝑎 < 0.05] = 0.890) however, it fails the model chisquare test (𝜒2=447.852,𝑑𝑓 = 209, 𝑝 = 0.000). According to Marôco (2014) the 𝜒2 test is heavily influenced by the sample size (among other factors, e.g. correlation between observed variables), so a model can be rejected despite truly presenting a good adjustment to the data simply because of the sample size. When the sample presents a considerable dimension (𝑛 > 400) the 𝜒2 test very often leads to the wrong conclusion, in other words, it is very likely to be significant (𝑝 < 0.05).This may be happening on the presented model. Because of this problem and others, researchers have developed other absolute fit indices such as the goodness of fit index (GFI) and root mean error of approximation (RMSEA). GFI presented a good value at 0.934, while RMSEA was acceptable at 0.046. The relative fit index CFI (comparative fit index) was 0.970, thus showing evidence of a good model fit. Analyzing each specific path in Figure 3, we can see that three of the paths were not significant (5% was considered as the critical level of significance), these were: "Punishment Certainty → Attitude" (𝛽𝐴𝑡𝑡.𝑃𝐶 = 0.009;p = 0.846); "Punishment Severity→ Attitude"(𝛽𝐴𝑡𝑡.𝑃𝑆 = 0.021;p = 0.624); and "Attitude→ Intention"(𝛽𝐼𝑛𝑡.𝐴𝑡𝑡 = 0.087;p = 0.089). The remaining TPB components had a significant but moderated effect on intention(𝛽𝐼𝑛𝑡.𝑆𝑁 = 0.257;p < 0.01; 𝛽𝐼𝑛𝑡.𝑃𝐵𝐶 = 0.358;p < 0.01), with moral obligation also having a significant but moderated effect on intention(𝛽𝐼𝑛𝑡.𝑀𝑂 = −0.307;p < 0.01). Attitude had a significant but moderated effect on perceived behavioral control(𝛽𝑃𝐵𝐶.𝐴𝑡𝑡 = 0.349;p < 0.01), and attitude remaining antecedents exhibited a significant but small effect(𝛽𝐴𝑡𝑡.𝑃𝑉 = 0.118; p = 0.016; 𝛽𝐴𝑡𝑡.𝐷𝑀𝐶 = 0.155;p < 0.01). Punishment certainty had a moderated effect on perceived behavioral control(𝛽𝑃𝐵𝐶.𝑃𝐶 = −0.348;p < 0.01). Finally, the path "Moral Obligation → Attitude" was the one that presented the higher path coefficient(𝛽𝐴𝑡𝑡.𝑀𝑂 = −0.699;p < 0.01). The final full model explains 63% of the variance in digital piracy intention. 41 4.2.2. Pirate Model This second model considers only those students who had pirated and therefore, the sample was smaller adding up to 421 entries. 4.2.2.1. Measurement Model The final CFA pirate model is presented in Figure 4, while the initial one is presented in Appendix C. To get to this final model we yet again follow Marôco (2014). The first step was to analyze factor validity, unfortunately the same three indicators (SN2, PV1, DMC3) failed again to fulfill the required conditions and were removed from the model. The Skew and Kurtosis coefficients showed adequate values that made possible to admit a normal distribution for almost all observed variables, the exception was PBC4 and consequently was removed. The existence of outliers assessed by Mahalanobis square distance displayed five cases with values suggesting that these were outliers, so the CFA was conducted without them. The model was then adjusted using the modification indices (greater than 11), a set of suggested modifications were related to the covariance between the error terms of the following indicators: ATT1 (e4) and ATT3 (e6); ATT3 (e6) and ATT4 (e7); MO1r (e13) and MO3 (e15). The suggested trajectories were added to the model since that all relations are between items that load on the same factor. The correlation between the errors may be occurring because of the similarity of wording and content. The fit between the data and the final CFA pirate model was overall considered as good (𝑋2𝑑𝑓 = 1.761; ⁄𝐶𝐹𝐼 = 0.969; 𝐺𝐹𝐼 = 0.926;𝑅𝑀𝑆𝐸𝐴 = 0.043;𝑃[𝑟𝑚𝑠𝑒𝑎 < 0.05] = 0.966;𝑀𝐸𝐶𝑉𝐼 = 1.580 ). Established a good model fit, construct reliability, convergent and discriminant validity were analyzed. All factors presented an adequate reliability and demonstrated convergent as well discriminant validity (see Appendix D). 42 Figure 4. Final CFA Pirate Model (𝐗𝟐𝐝𝐟 = 𝟏.𝟕𝟔𝟏; ⁄𝐂𝐅𝐈 = 𝟎.𝟗𝟔𝟗; 𝐆𝐅𝐈 = 𝟎. 𝟗𝟐𝟔;𝐑𝐌𝐒𝐄𝐀 = 𝟎.𝟎𝟒𝟑;𝐏[𝐫𝐦𝐬𝐞𝐚 < 𝟎.𝟎𝟓] = 𝟎. 𝟗𝟔𝟔;𝐌𝐄𝐂𝐕𝐈 = 𝟏. 𝟓𝟖𝟎 ). 43 4.2.2.2. SR Model The final SR pirate model (Figure 5) revealed a satisfactory model fit (𝜒2= 447.852, 𝑑𝑓 = 209,𝑝 = 0.000; 𝑋2𝑑𝑓 ⁄= 1.960; 𝐶𝐹𝐼 = 0.957; 𝐺𝐹𝐼 = 0.910; 𝑅𝑀𝑆𝐸𝐴 = 0.048;𝑃[𝑟𝑚𝑠𝑒𝑎 < 0.05] = 0.694). However, when we examine each specific path we can see that six paths were not significant, these were: i. "Perceived Value → Attitude" (𝛽𝐴𝑡𝑡.𝑃𝑉 = 0.096;p = 0.078); ii. "Digital Media Cost → Attitude" (𝛽𝐴𝑡𝑡.𝐷𝑀𝐶 = 0.083;p = 0.083); iii. "Punishment Certainty → Attitude" (𝛽𝐴𝑡𝑡.𝑃𝐶 = 0.056;p = 0.279); iv. "Punishment Severity→ Attitude"(𝛽𝐴𝑡𝑡.𝑃𝑆 = −0.006;p = 0.897); v. "Attitude→ Intention"(𝛽𝐼𝑛𝑡.𝐴𝑡𝑡 = 0.042;p = 0.465); and vi. "Subjective Norms → Intention" (𝛽𝐼𝑛𝑡.𝑆𝑁 = 0.084;p = 0.118). The decision to use the standard level of significance instead of 10% culminated in the rejection of some hypothesis that otherwise would not be rejected, this is true for both models and it is important to take in account when comparing the conclusions here presented with other authors. The remaining TPB component had a significant but weak effect on intention(𝛽𝐼𝑛𝑡.𝑃𝐵𝐶 = 0.124;p = 0.003). Moral obligation had a significant but moderated effect on intention(𝛽𝐼𝑛𝑡.𝑀𝑂 = −0.304;p < 0.01), while past piracy behavior presented a substantial effect on intention (𝛽𝐼𝑛𝑡.𝑃𝑃𝐵 = 0.490; p < 0.01). Punishment certainty and past piracy behavior had a significant effect on perceived behavioral control, the first having a moderated effect and the second a significant one (𝛽𝑃𝐵𝐶.𝑃𝐶 = −0.237;p < 0.01; 𝛽𝑃𝐵𝐶.𝑃𝑃𝐵 = 0.490;p < 0.01). Finally, the path "Moral Obligation → Attitude" was the one that presented the higher path coefficient(𝛽𝐴𝑡𝑡.𝑀𝑂 = −0.712;p < 0.01). The final pirate model explains 70% of the variance in digital piracy intention. 44 Figure 5. Final Pirate SR Model. Path coefficient estimates are reported as standardized (∗∗p<0.01; ∗0.01≤p≤0.05; ns (not significant)p>0.05) 45 5. Discussion and Implications 5.1. TPB Variables, Moral Obligation and Past Piracy Behavior Attitude toward the behavior is a personal factor that evaluates an individual’s predisposition toward performing digital piracy. It was hypothesized that individuals with a more positive attitude towards piracy will correspond to a greater intention to pirate digital materials. However, contrary to expectations attitude was not a significant predictor of intention in both models, as so hypothesis H1 is rejected. This may be due to the influence of moral obligation, which had a strong negative effect on attitude in both models and might diminished attitude’s positive effect on intention and correspondent significance. This effect was not expected, but it makes sense, suggesting that if someone views digital piracy as morally wrong, then his attitude would be negatively influenced. This result does not support previous researchers that have found attitude a significant precursor to intention (see for example Peace et al., 2003; D'Astous et al., 2005; Al-Rafee and Dashti, 2012), presenting a moderated/high effect on it. Usually, is considered that by altering attitude it should be possible to reduce piracy, thus making attitude a very important variable in the fight against piracy and making it more difficult since attitude was not a significance factor. It may also indicate that individuals pirate despite presenting an unfavorable attitude toward digital piracy. The remaining TPB components in the full sample model presented the expected outcome. It was hypothesized that individuals with a higher level of subjective norms supportive/(perceived control over performance) of piracy will correspond to a greater intention to pirate digital materials. The results showed that subjective norms and perceived behavioral control had a significant but moderated effect on intention. As such, hypotheses H2 and H3 are not rejected, and we conclude that: i) the approval of digital piracy by friends, family (or any significant others) positively affect the individual’s intention; ii) that subjects that find easy to pirate and have the opportunity to do so, will most likely have a greater intention to pirate digital materials. The pirate model yield a similar result regarding perceived behavioral control, but the other variable, subjective norms, was not a significant 46 predictor of intention. Thus, it is possible that those who have pirated before may not be influenced by perceived social pressures. Hypothesis H4 states that the higher the feeling of moral obligation, the lower is an individual intention to pirate digital materials. Examining the results, this hypothesis is not rejected for both models, with moral obligation having a significant and negative effect on intention. This negative relation enables to conclude that individuals with a higher sense of morality will tend to have a lower intention towards pirating. As we can see, it appears that moral obligation and perceived behavioral control play a key role in digital piracy, being significant predictors of intention in both models, making the connection between them and presenting themselves as the ideal factors to “attack”. A possible approach is to use an individual’s moral obligation or feelings of guilt to show that piracy is not only affecting company’s earnings but ultimately is a major issue for the whole society with all of us losing, not allowing more jobs (or even destroying current one’s) and taxes that could be used to directly improve people’s lives. At last, it was hypothesized that there is a positive relationship between past piracy behavior and intention. This was indeed true, with past piracy behavior presenting a substantial effect on intention, hypothesis H5 was not rejected. As so, it is expected that individuals that pirated digital material in the past are more likely to incur in the same intentions. Past piracy behavior also revealed a significant and strong positive relation with perceived behavioral control, this relation shows that with experience we get comfortable doing a certain task, our sense of control gets higher. Indeed, 40.4% of the students disclosed that they pirate a lot, and 25.4% does it in a daily base or almost daily, all this shows that past behavior has a strong and determinant influence on control and intention. Nowadays we can access the internet virtually anywhere and download whatever we want or even give orders to our computer at home to start a download, making pirating so easy that can become recurrent and ultimately a habit. This makes past piracy behavior a very difficult factor to address. A suggestion is to restrict the number of places where people can access websites that facilitate the download or streaming of pirate content. For 53 8. References AI-Rafee, S. and Cronan, T. P. (2006), "Digital Piracy: Factors that Influence Attitude Toward Behavior", Journal of Business Ethics, Vol. 63 No. 3, pp. 237-259. Ajzen, I. (1985), "From intentions to actions: A theory of planned behavior", in Kuhl, J. and Beckman, J. (Eds.), Action-control: From cognition to behavior. 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Mesmo que nunca tenha pirateado qualquer tipo de material digital poderá preencher o questionário sem qualquer dificuldade. Agradeço a sua participação, pois é indispensável para o desenvolvimento do meu trabalho e solicito que responda a todas as questões com a máxima sinceridade. A participação neste estudo é voluntária, e será assegurada a confidencialidade de todas as respostas. Idade:____ Sexo (M/F):____ Estudante de: Ensino Secundário Ensino Profissional Licenciatura Mestrado Doutoramento Pós-Graduação Ocupação: Estudante a tempo inteiro Trabalhador-Estudante Outra:____________________ O que é a Pirataria Digital? Entende-se por Pirataria Digital o download/cópia de forma ilegal de software e ficheiros de media protegidos por direitos de autor. Tais ficheiros podem ser filmes, musica, vídeo jogos, entre outros. Como preencher o questionário?  O seguinte questionário tem como objetivo avaliar o grau de concordância ou discordância com cada uma das questões/afirmações apresentadas.  Todas as repostas serão medidas numa escala de 7 pontos. Ao selecionar a quadrícula próxima dos extremos, significa que concorda com o adjetivo (ou afirmação) próximo da sua escolha, à medida que se afasta dos extremos a sua concordância vai diminuindo, representando a quadrícula central, indiferença (ou neutralidade).  Responda a cada questão selecionando a opção pretendida com uma cruz. 61 Considero que de um modo geral que a pirataria digital é: Favorável ☐ ☐ ☐ ☐ ☐ ☐ ☐ Desfavorável Benéfica ☐ ☐ ☐ ☐ ☐ ☐ ☐ Prejudicial Sensata ☐ ☐ ☐ ☐ ☐ ☐ ☐ Insensata Boa ☐ ☐ ☐ ☐ ☐ ☐ ☐ Má O seguinte conjunto de perguntas procura aferir a sua capacidade para piratear Para mim piratear material digital seria/é: Muito Fácil ☐ ☐ ☐ ☐ ☐ ☐ ☐ Muito Difícil Querendo facilmente poderia piratear material digital Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente Considero-me capaz de piratear material digital Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente Tenho os recursos necessários (ex. computador, ligação à internet, etc.) para piratear material digital Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente Se quiser sou capaz de encontrar material digital para piratear Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente O seguinte conjunto de perguntas encontra-se relacionado com a sua atitude de pirataria no passado e a sua intenção de piratear no futuro Pirateei material digital no passado (Se responder não, salte as duas próximas questões) Sim ☐ ☐ Não Quanto material digital pirateou? Muito ☐ ☐ ☐ ☐ ☐ ☐ ☐ Pouco Qual a frequência com que pirateou material digital Diariamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Esporadicamente 62 Tenciono piratear material digital num futuro próximo Certamente que Sim ☐ ☐ ☐ ☐ ☐ ☐ ☐ Certamente que Não Se tivesse a oportunidade, piratearia material digital Certamente que Sim ☐ ☐ ☐ ☐ ☐ ☐ ☐ Certamente que Não Farei todos os esforços para piratear material digital num futuro próximo Certamente que Sim ☐ ☐ ☐ ☐ ☐ ☐ ☐ Certamente que Não As questões abaixo procuram fornecer um melhor entendimento da opinião daqueles que considera como importantes na sua vida (exemplo: familiares e amigos) As pessoas que considero importantes na minha vida pensam que não devo piratear material digital Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente Relativamente à pirataria digital, penso que devo fazer o que as pessoas que me são importantes consideram como correto Concordo Plenamente ☐ ☐ ☒ ☐ ☐ ☐ ☐ Discordo Completamente Se piratear material digital, a maioria das pessoas que me são importantes iriam: Não se Importar ☐ ☐ ☐ ☐ ☐ ☐ ☐ Desaprovar O seguinte conjunto de perguntas está relacionado com a severidade da punição, no caso de ser apanhado a piratear Se fosse apanhado a piratear penso que a punição seria: Muito Elevada ☐ ☐ ☐ ☐ ☐ ☐ ☐ Muito Baixa Se fosse apanhado a piratear, seria severamente punido Concordo Plenamente ☐ ☐ ☐ ☐ ☐ ☐ ☐ Discordo Completamente 69 Appendix E.: Model fit tests and correspondents reference values. Model Fit Test Reference Values 𝑿𝟐 and p-value The lower the better; 𝑝 > 0.05 𝑿𝟐𝒅𝒇 ⁄ < 5 − Bad fit ]2;5] − Acceptable fit ]1;2] − Good fit ~ 1 − Very good fit CFI GFI < 0.8 − Bad fit ]0.8;0.9] − Acceptable fit ]0.9;0.95] − Good fit ≥ 0.95 − Very good fit RMSEA and p-value (𝑯𝟎: 𝒓𝒎𝒔𝒆𝒂 ≤ 𝟎.𝟎𝟓) > 0.10 − Unacceptable fit ]0.05;0.10] − Acceptable fit ≤ 0.05 − Very good fit 𝑝 ≥ 0.05 MECVI To compare models. The lower the better Source: Marôco (2014, p.55).