Social Research Methods
A Quantitative Research to Determine the Factors Affecting Intention to Commit Digital Piracy
HRM Section A - Group 1 Aaditya Narayan Chaudhary Aayush Goel Abhay Kumar Vasishtha Abhishek Saxena Akshay Maxim Sequeira
H13001 H13002 H13003 H13005 H13009
Executive Summary The main aim of conducting this research project is to understand the intention to commit digital piracy among business school students in India. In order to study the final intention of a student to commit digital piracy we have identified several independent variables such as attitude, moral obligation, perceived behavioral control etc. and through a study on the relevant literature review have designed a questionnaire which aims to capture the data about these constructs. It is our final aim to narrow down on a few key independent variables that have maximum impact on a persons’ intention to commit digital piracy. We then can analyze the most appropriate corrective action that can be taken to remedy the problem. After the data analysis of the survey, we found that intention to commit digital piracy can be significantly impact by perceived behavioral control, overall attitude towards piracy and perceived psychosocial risk. Thus, the best way to curb the malice of digital piracy is to take measure to increase the perceived psychosocial risk among people while decreasing the overall attitude towards piracy.
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Contents Introduction ......................................................................................................................................................... 3 Problem Setting ................................................................................................................................................... 6 Literature Review ................................................................................................................................................ 7 Conceptual Background for the Research Objectives ..................................................................................... 7 Attitude towards Digital Piracy .................................................................................................................... 7 Perceived Risk .............................................................................................................................................. 8 Perceived Behavioral Control ...................................................................................................................... 9 Moral Obligation.......................................................................................................................................... 9 Subjective Norms ....................................................................................................................................... 10 Intention to Commit Digital Piracy ............................................................................................................ 10 Social Desirability Bias ............................................................................................................................... 11 Computer Efficacy ..................................................................................................................................... 11 Major Hypotheses Used ................................................................................................................................ 12 Research Model ................................................................................................................................................. 13 Design ................................................................................................................................................................ 14 Questionnaire Design .................................................................................................................................... 14 Questionnaire Details .................................................................................................................................... 14 Source of the questionnaire .......................................................................................................................... 14 Questionnaire Wording ................................................................................................................................. 16 Response Choices .......................................................................................................................................... 16 Question Sequence........................................................................................................................................ 16 Questionnaire Pretesting............................................................................................................................... 16 Administering the Questionnaire - Sample Design and Response Rate ........................................................ 17 Analysis and Discussion of Results .................................................................................................................... 18 Implications of the Study Findings .................................................................................................................... 37 References ......................................................................................................................................................... 40 Appendix ............................................................................................................................................................ 43
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Introduction Digital piracy encompasses the illegal copying and/or downloading of copyrighted software or digital materials such as music, movies, files and books. The practice of digital piracy has posed a significant threat to the development of the software industry and the growth of the digital media industry. Piracy is an impending problem for India’s media and entertainment industry causing losses of about INR 20,000 crore every year due to copyright infringement. India’s digital music market is in the range of INR 5,000 crore, but only 7 percent of it is legitimate. The commercial value of unlicensed software installed on personal computers in India was estimated at $2.74 billion in 2010. The Indian Movie Industry is facing a big problem in terms of piracy, with movie prints becoming available online within a couple of days of the movies’ release and this has caused a huge loss to the distributors. The copy of the film is also available in the grey market at a price that is ten times lesser than the original. Even book piracy has reached an alarming level with publishers losing as high as 25% to piracy. The Torrent downloader is popular among urban Indian youth, who access content such as films, music and software “illegally” from across the world through this software. IMI, an umbrella organization of over 140 music companies, including market leaders like Saregama, Universal Music, Sony Music, Venus, and Tips have served notices to ISPs like Vodafone, Bharti Airtel and Reliance Communications to immediately stop reproducing, distributing and transmitting sound recordings which infringes on their copyrights. The Indian music industry is elated with the decision of the Kolkata High Court that directs Internet Service Providers (ISPs) to block websites that allow illegal downloading of songs. Companies like Moser Baer, the world’s second largest manufacturer of optical storage media like CDs and DVDs is constantly looking forward to fight piracy with unique distribution strategies, aggressive marketing, competitive pricing and urging customers to “Kill Piracy”. The company adopts a unique model wherein it picks up home video distribution rights of films from small distributors who are willing to give away their films at lesser price and then sell these to customers at low prices. It is also aggressively looking at collaborating for new releases and in one such deal valued at INR 250 million, it acquired home video release rights to UTV’s home video catalog which included 10 films. Flipkart, India’s most successfulve-commerce company is also exploring options for selling legitimately licensed digital music. Even Saavn.com, a digital distributor of music offers free streaming with a catalog of over 200,000 songs in various Indian languages. Its mobile application can be used in both the iPhone and Android formats and is a huge hit among music enthusiasts. The India wing of the Business Software Alliance (BSA) has been a key agent in sensitizing the issue on software piracy and assisting the police in conducting raids on pirates around the country. The Motion Pictures Association (MPA) has been instrumental in keeping a check on film piracy in India. In addition, major movie production houses ranging from Red Chillies Entertainment (owned by Shah Rukh Khan), Yash Raj Films, UTV Motion Pictures have also formed coalitions to deal with piracy and have sought assistance of former intelligence agents and police officers to curb the menace of digital piracy by conducting raids all over the country. Leading producer Mr. Mukesh Bhatt mentioned that for his movie 'Aashiqui 2', more than 40 percent of my revenue was lost to internet piracy. It is also
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estimated that Vishal Bhardwaj’s Kaminey, was downloaded 350,000 times on Bit Torrent with about 2/3rds of downloaders being from India. An article published in Mint has revealed some shocking facts that piracy and counterfeiting are growing rapidly in India and have deprived the Indian entertainment industry of $4 billion and around 820,000 jobs. The big question that worries producers, software developers and the like is how to change the mindset of the Indian customer who is so accustomed to pirate and distribute digital material. Additionally, implementing the free streaming approach and the pay-for-download approach have their own challenges. Downloads require users to have a credit card or electronically transfer money into a digital wallet. To market this approach to the mass media is a big problem in itself. Meanwhile, free ad-supported service faces the hurdle of attracting spending from Indian marketers who are still quite satisfied advertising on TV and in newspapers. Google is planning to launch a music service in India that will allow users to search for legal music streams online and in this will help in curbing the already rampant practice of digital piracy. The users will be able to search for and instantaneously listen to many songs which will be delivered by Google’s partners in India. This service will be free of charge. Online book business by direct tie-ups with publishers such as Pearson Education, Wiley India, Tata McGraw Hill and Penguin Books India will help curb book piracy and promote the sale of original copies. To accelerate the rate of decline in software piracy, there is a need to establish a strong enforcement agency in the country that specializes in the subject of Intellectual Property Rights (IPRs) and to create general awareness on issue of copyright, piracy and IP. In India, the Indian Copyright Act 1957 protects IP owners in a traditional infringement context which has been extended to digital and online infringing activities, through provisions in the Information Technology Act 2000. The Copyright Amendment Bill, 2010 is expected to remove operational difficulties in implementing intellectual copyrights, and will address new issues concerning the digital world and the internet. The 3 tables below show the prices of licit and illicit DVD prices in India, and the most popular download categories from DCTorrent and DesiTorrents.
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Problem Setting The major concern in today’s leadership context is producing good, ethical leaders who can guide the future towards a brighter direction. However, modern unethical practices among executives is rampant and this phenomenon is only set to grow. As B-School students whose college espouses the motto of integrity and excellence, we intend to find out whether future leaders in B-schools also adhere to the philosophy of ethical integrity. To measure the same, we have used digital piracy as a surrogate to determine the level of ethical inclination of B-school students. During the last decade, much research has been dedicated to the study of ethics and ethical behavior in business. Ethical situations arise often in many different areas of business, and this has been complicated by the integration of Information Systems (IS) into business operations. One issue that has been in the news lately is the issue of intellectual property and specifically, digital piracy. As a multi-billion dollar industry, it is thriving on the hyper connected youth who demand for the latest products and services either digitally or online and prefer to obtain pirated materials. While software piracy has received much interest (with an estimated $ 13 billion in lost revenues in 2002) (Business Software Alliance, 2003), a new form of piracy has taken the piracy spotlight and being called the next big piracy arena (Bhattacharjee et al., 2003). Referred to as Digital Piracy, and defined in this paper as, "the illegal copying/downloading of copyrighted software and media files". According to the Forrester research group (http://www.forrester.com), lost revenues due to digital piracy could reach $5 billion alone from music and book publishers by the year 2005 (not counting losses from software companies or cinema studios). The next big piracy target apparently will be Hollywood, as the Motion Picture Association of American (MPAA) estimates that around 400,000 600,000 movies are being copied/downloaded on the Internet every day (MPAA Report, 2003). The purpose of this study is to identify factors that influence an individual's intention to commit digital piracy. While much of the previous research concentrated on the piracy behavior and how to control it (Conner and Rumlet, 1991; Glass and Wood, 1996; Gopal and Sanders, 1997; Moseley and Whitis, 1995), this study examines the factors that influence the intention regarding such a behavior. By doing so, measures to alter those factors can be implemented (and thus influence behavior indirectly) that would reduce digital piracy – a current problem. This is especially important since many studies have suggested that individuals do not see piracy as a crime or an unethical issue. A better understanding of these factors that influence intention toward digital piracy could prove to be essential in our understanding of this phenomenon and help us combat digital piracy.
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Literature Review Conceptual Background for the Research Objectives The main research objective of this project is to identify the critical parameters or the main independent constructs that influence a persons’ intention to commit digital piracy. In order to achieve this objective we needed to identify certain independent constructs through an extensive research survey. The independent constructs that we have selected for our project are attitude, moral obligation, subjective norms, perceived risk, computer efficacy and perceived behavioral control. In order to operationalize these constructs we further tried to analyze them from our knowledge of organizational behavior and with the help of existing research papers. For example: from our study of organizational behavior we understand that attitudes are general evaluative statements about a person makes about a particular behavior and to fully operationalize this construct we further needed to understand its sub components. In this case we identified the three parts as cognitive, affective and behavioral. This analysis helped us choose questions from existing surveys to best design our model. This process was followed for all our independent constructs. Attitude towards Digital Piracy
From our study of organizational behavior we understand that an attitude in general is an evaluative statement either favorable or unfavorable about objects people or events. They reflect the way we feel about something and it is important in our study of digital piracy behavior because attitude is one of the independent variable that affect the intention to commit digital piracy. A lot of firms have lost significant revenues to piracy. A major reason for studying attitude and considering it as one of our independent constructs is that attitude can be changed through persuasion and other means. Since attitude is one of the factors influencing the intention to commit digital piracy if we can find that there is relationship between them it would be worth exploiting the fact that a change in attitude towards digital piracy will go a long way in curbing people’s intention to commit piracy. Again from our study of organizational behavior we understand that researchers have assumed that attitudes have three components; cognition, affect and behavior. The cognitive component refers to the opinion or belief segment of an attitude. The affective component measures the emotional or feeling segment of an attitude. The behavioral component measures the tendency to behave in a certain way toward someone or something. In order to effectively study the effect of attitude on the intention to commit piracy we first need to measure the attitude in an effective manner. For the purpose of our study, we will be considering attitude as an independent variable and examining its effect on the intention to commit digital piracy. We have hence based our literature review and study in a way that we could capture all the three elements of attitude. The original theory of reasoned action (Fishbein and Ajzen, 1975) and the theory of planned behavior (Ajzen, 1985) assert that intention is determined by attitude. Other empirical studies confirm that attitude has a significant impact on intentions (see Ajzen,1991; Sheppard et al., 1988 for reviews). It is a relatively straightforward leap of logic to conclude that this relationship holds true for piracy. This gives us the basis for measuring the cognitive part of attitude from the following three questions.
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1. Using copied software is a bad idea 2. I dislike the idea of using copied software 3. Using copied software is a wise idea The above questions are measured on a 7 point Likert scale (1 being strongly disagree and 7 being strongly disagree). The Affective Beliefs measure, Bodur et al. (2000), used four categories (arousal, elation, pleasantness, and distress) to assess their affective beliefs construct. Other researchers have used a two-dimensional structure based on pleasure and arousal (see Bodur et al., 2000). In our study, a three-dimensional affective structure is used to measure affective beliefs - excitement (arousal), happiness (pleasantness), and distress. Distress was also used since it is an element of nervousness/fear when a subject downloads/ copies digital material illegally (as a result of the illegality of the behavior or not knowing whether one was downloading a virus, for example). We have adapted the construct on Attitude towards Piracy from Al-Rafee & Cronan (2006), Cronan & AlRafee (2008) and Goles et al (2008) and the items included are associated with happiness, excitement, fear and nervousness. Perceived Risk
The concept of perceived risk was first introduced by Bauer (1960) when he characterized consumer choice in terms of risk-taking or risk-reducing behavior (Tan, 2002). Bauer (1960) emphasizes that he is concerned only with perceived risk (subjective risk) and not actual risk (objective risk) because consumers are bounded rational actors that do not perform actual mathematical calculations of risk (unlike actuaries or accountants) and rather form subjective risk beliefs based on internal and external information (Featherman et al., 2006). According to Bauer (1960), a person's behavior involves risk if the behavior will produce consequences that he or she cannot anticipate with anything approximating cer tainty and some of which are likely to be unpleasant. In the marketing literature, perceived risk is conceptualized as involving two elements: uncertainty and consequences (Campbell and Goodstein, 2001; Conchar et al., 2004; Cunningham, 1967; Dowling and Staelin, 1994; Jacoby and Kaplan, 1972; Laroche et al, 2005). Perceived risk arises when an individual is engaged in situations where the outcomes are never totally certain and is concerned about the consequences of a poor or wrong decision (Fraed rich and Ferrell, 1992; Havlena and DeSarbo, 1991). The perceptions of risk are considered to be central to a person's evaluations, choices and behaviors (Campbell and Goodstein, 2001). In general, people are prone to avoid mistakes rather than maximize utility when engaging in risky decision-making. Perceived risk is therefore a powerful tool to explain individual behavior (Mitchell, 1999). There have been numerous studies, both theoretical and empirical , identifying risks as critical factors influencing consumer decision making (e.g., Featherman and Pavlou, 2003; Fraedrich and Ferrell, 1992; Jacoby and Kaplan, 1972; Mitchell, 1992; Pavlou, 2003). In recent studies (e.g., Fraedrich and Ferrell, 1992; Tan, 2002), perceived risk is also considered a key variable in determining ethical decision making. Although perceived risk reveals various meanings and dimensions in different research contexts, most of the scholars view perceived risk as a multidimensional construct. For example, Cunningham (1967) divided perceived risk into six risk facets namely performance risk, financial risk, opportunity/time risk, psychological risk, social risk and
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safety risk. Jacoby and Kaplan (1972) identified five risk dimensions including financial, performance, physical, psychological and social risks and found that the five risk dimensions account for 61.5% of the total variance in the overall risk measure. Other researchers have also suggested that time risk is an important risk dimension (e.g., Roselius, 1971; Stone and Gronhaug, 1993). In addition, Tan (2002) used prosecution risk instead of physical risk and proposed that performance, financial, social and prosecution risks are the most important aspects of risk. We have included the construct on Perceived Risk from Liao et al (2010). Perceived Behavioral Control
The Theory of Reasoned Action (TRA) was formulated based on the premise that intention is the best predictor of behavior (Fishbein and Ajzen, 1975). The TRA is based on the notion that human behavior is rational and makes use of the limited information available to individuals. The TRA asserts that attitude and subjective norms are the two determinants that affect human behavior. The Theory of Planned Behavior (TPB) is an extension of the TRA, introduced by Ajzen in 1985. Ajzen explains that the TRA is insufficient because it does not consider situations where the behavior is not under the individual's control. To ensure accurate prediction of behavior over which individuals have only limited control, the estimate of the extent to which the individual is capable of exercising control over the behavior in question is also essential (Ajzen and Madden, 1986, p. 456). The model presented by Ajzen includes an additional determinant of intention, called Perceived Behavioral Control. Perceived behavioral control represents the person's belief of how easy or difficult it is to perform the behavior (Ajzen and Madden, 1986). Limayem et al. (1999) used a longitudinal design to study piracy within business students. The study included a variable viz. perceived consequences/ beliefs to explain the behavioral process. The results of the study indicated that only social factors and perceived consequences had an influence on the piracy behavior. We have included the construct on Perceived Behavioral Control from Cronan and Al-Rafee (2008). Moral Obligation
Moral obligation as a deontological concept refers to the feeling of guilt or the personal obligation to perform or not to perform a behavior (Cronan and Al-Rafee, 2008). This factor has been used in IT ethics literature to predict moral intention (Haines and Leonard, 2007). Moral obligation has also been proposed as an affecting intention in studies within the psychology field (see Ajzen, 1991). Cronan and Al-Rafee (2008) state that moral obligation is significant predictor of intention in digital piracy. Also, according to Fishbein and Ajzen (1975), subjective norms are a function of the product of one’s normative beliefs and his/her motivation to comply with that referent. Moral obligation as a normative ethical standard may play a role in forming personal normative beliefs as a basis. Personal moral obligation is an individual’s moral stance about performing that behavior, or how the individual feels about performing the behavior, as opposed to his evaluation of the outcomes of performing the behavior (Beck and Azjen, 1991). Personal moral obligation reflects whether the
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individual feels guilty because he violated an internalized norm, or does not feel guilty because the behavior was consistent with the norm. This feeling of guilt (or lack thereof) may be particularly relevant in cases of socially questionable activity (Grasmick and Scott, 1982; Peace and Galletta, 1996). Moral Obligation (MO) refers to the feeling of guilt or the personal obligation to perform or not to perform a behavior. This factor has been used in the research literature to predict ethical intention (Randall and Gibson, 1991; Kurland, 1995; Banerjee et al., 1998; Leonard and Cronan, 2001). MO has also been theorized as affecting intention in studies within the psychology field. Ajzen (1991) indicated that moral obligation could possibly be added to the TPB as a separate determinant of intention. In a review of TPB research, Conner and Armitage (1998) found that moral obligation was a significant predictor of intention in a number of studies. The digital piracy case presents a situation where individuals who are contemplating piracy could very well process guilt or personal obligation to pirate/ not pirate digital material. Given the recent media exposure regarding the seriousness of digital piracy and public awareness attempts, individuals could form intentions with a moral obligation factor in mind. Once again, behavior could be based on reason with guilt or obligation affecting the intention to pirate or not pirate. Research is needed to determine what effect (if any) moral obligation has on the intention to pirate. We have adapted the construct on Moral Obligation from Yoon, C. (2011), Goles et al (2008) and Cronan & Al-Rafee (2008). Subjective Norms
There is evidence that suggests that subjective norms also influence intention to pirate (Chang, 1998; Shepherd and O'Keefe, 1984; Shimp and Kavas, 1984; Vallerand et al., 1992). Since one's attitude (or ethical attitude) towards a specific behavior is likely to be influenced by significant others (Bommer et al., 1987; Kreie and Cronan, 1999a, b), Subjective norms are theorized to influence inclination towards piracy. The higher the evaluation of subjective norms (significant others have a favorable opinion towards the behavior, the higher the inclination to commit piracy). Subjective norms have been assessed by asking subjects whether significant others approve or disapprove their behavior in question. Items include questions such as "Most people who are important to me think that I should not pirate digital content", and "When considering digital piracy, I wish to do what most important people to me think" (Ajzen, 1991). We have adapted the construct on Subjective Norms from Cronan & Al-Rafee (2008), Yoon, C. (2011) and Wang et al (2009).
Intention to Commit Digital Piracy
Fast Internet connections, availability of inexpensive high capacity storage, and underground peerto-peer networks on the Internet which are impossible to control have led to a mass increase in piracy. Software Piracy 2.0 (Bhattacharjee et al., 2003) is an extended version of piracy that refers to the recent phenomenon of pirating music, movies, and e-books in addition to software. This type of piracy is referred to as "digital piracy" and defined as "the illegal copying/download of copyrighted software and media files".
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To better understand why digital piracy behavior occurs, researchers should determine what factors that affect the intention to pirate. Straub and Collins (1990) identified software piracy as a major problem facing the technology industry today and offered deterrents to piracy. Anderson et al. (1993) examined piracy and intellectual property as some of the top issues facing IS professionals. Past Behavior is one of the determinant of the intention to commit digital piracy (Bagozzi, et al., 1992; Ajzen, 2002b; Hagger et al., 2002; Bamberg et al., 2003). The research done by Bamberg et al. (2003, p. 186) states that past behavior is not always a good predictor of future behavior. They suggest that only when circumstances are relatively stable, prior behavior makes a significant contribution to the prediction of later behavior. Conner and Armitage (1998) have theorized that past behavior as a predictor of intention. There may be situations in the digital piracy case where the action of piracy is routine and habits may or may not exist. Simpler technological initiatives, change in laws and regulations have led to a change as to how digital piracy is performed. We have included the construct on ‘Intention to Commit Digital Piracy’ from Cronan and Al-Rafee (2008) and the effect of independent determinants has been studied on this. Social Desirability Bias
Social desirability is commonly thought of as the tendency of individuals to project favorable images of themselves during social interaction. Short version of the questionnaire has been adapted from Marlowe–Crowne Social Desirability Scale (Crowne & Marlowe, 1960). Computer Efficacy
The concept of computer efficacy can be better understood by first understanding about an important construct in social psychology viz. self-efficacy-- the belief that one has the capability to perform a particular behavior. Self-efficacy perceptions have been found to influence decisions regarding what behaviors to undertake (e.g., Bandura, et al., 1977; Betz and Hackett, 1981), the effort exerted and the persistence in attempting those behaviors (e.g., Barling and Beattie, 1983; Brown and Inouye, 1978), the emotional responses of the individual performing the behaviors (e.g., Bandura, et al., 1977; Stumpf, et al.,1987), and the actual performance attainments of the individual with respect to the behavior (e.g., Barling and Beattie, 1983; Collins, 1985; Locke, et al., 1984; Schunk, 1981; Wood and Bandura, 1989). The relationship between self-efficacy (with respect to computers) and a variety of computer behaviors have also been examined (Burkhardt and Brass, 1990; Gist, et al., 1989; Hill, et al., 1986; 1987; Webster and Martocchio, 1992; 1993). Having a reliable and valid measure of self-efficacy leads to successful implementation of support, training and systems in organizations. Computer efficacy refers to a person’s beliefs about his/her abilities to efficiently use computers. Computer self-efficacy exerts a significant influence on an individuals' expectations about using computers, their emotional reactions to computers (affect and anxiety), and their actual computer use (Compeau and Higgins, 1995). This research paper also demonstrates that an individual's selfefficacy and outcome expectations are positively influenced by the encouragement of others in their work group and their use of computers.
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The Theory of Reasoned Action (Fishbein and Ajzen, 1975) states that individuals would use computers if they feel that there would be positive benefits/outcomes associated with using them. There exists a relationship between self-efficacy and registration in computer courses at universities (Hill, et al., 1987), innovations (Burkhardt and Brass, 1990), adoption of high technology products (Hill, et al., 1986), and performance in software training (Gist, et al., 1989; Webster and Martocchio, 1992; 1993). We have included the construct on Computer Efficacy from Compeau and Higgins (1995) and have attempted to study the impact of computer efficacy on perceived behavioral control towards digital piracy.
Major Hypotheses Used The constructs adapted from various sources were subjected to Factor Analysis as a result of which we extracted 11 components – 9 independent, 1 intermediate and 1 dependent. The following hypotheses have been formed keeping in mind the literature review and the research gap that we were able to uncover from the existing literature. These hypothesis aim to measure impact of certain variables on the intention to commit digital piracy. H1: Encouraging attitude to commit digital piracy has a positive impact on the intention to commit digital piracy. H2: Overall attitude to commit digital piracy results in an intention to commit digital piracy. H3: Adverse attitude to commit digital piracy has a negative impact on the intention to commit digital piracy. H4: Perceived Psycho-social risk has a negative impact on the intention to commit digital piracy. H5: Perceived Functional risk has a negative impact on the intention to commit digital piracy. H6: Moral Obligations have a negative impact on the perceived behavioral control over digital piracy. H7: Computer Efficacy has a positive impact on the perceived behavioral control over digital piracy. H8: Perceived behavioral control has a negative impact on the intention to commit digital piracy. H9: Music Piracy Subjective Norms have a negative impact on the intention to commit digital piracy. H10: Digital Piracy Subjective Norms have a negative impact on the intention to commit digital piracy.
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Research Model Attitude Encouraging Attitude to commit Piracy
Subjective Norms
+
Overall Attitude to commit Piracy
Music Piracy Subjective Norms Digital Piracy Subjective Norms
-
Adverse Attitude to commit Piracy
-
-
Intention to Commit Digital Piracy
Perceived Behavioral Control
Perceived Risk
Perceived Psycho-social Risk Perceived Functional Risk
-
Moral Obligation
+ Computer Efficacy
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Design Questionnaire Design After an exhaustive literature review, the various dependent and independent constructs that affect the intention to commit digital piracy were obtained. The independent constructs identified were attitude towards digital piracy, perceived risk, perceived behavioral control, moral obligation, subjective norms and computer efficacy and these were found to be affecting the intention to commit digital piracy. Standard questionnaires that had been used in previous research papers were used for measuring the various constructs. The questions pertaining to respondent information were about the age, gender, years of work experience, college, roll no., average downloading per month and the major download category.
Questionnaire Details Construct
Scale Used for Measurement
No. of Items
Attitude towards piracy
7 point Likert scale
16
Perceived Risk
5 point Likert scale
10
Perceived behavioral control
7 point Likert scale
5
Moral Obligation
7 point Likert scale
9
Subjective Norms
7 point Likert scale
11
Intention to commit digital piracy
7 point Likert scale except Q4
5
Social-Desirability Scale
5 point Likert scale
6
Computer Efficacy
10 point Likert scale
10
Total items in main body of the questionnaire: 73 Total items relating to respondent characteristics: 7 Total items in the questionnaire: 79
Source of the questionnaire Subjective Norms
Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78(4), 527-545.
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Yoon, C. (2011). Theory of planned behavior and ethics theory in digital piracy: An integrated model. Journal of Business Ethics, 100(3), 405-417. Wang, C. C., Chen, C. T., Yang, S. C., & Farn, C. K. (2009). Pirate or buy? The moderating effect of idolatry. Journal of Business Ethics, 90(1), 81-93. Perceived Behavioral Control
Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78(4), 527-545. Intention to commit digital piracy
Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78(4), 527-545. Computer Efficacy
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2). Moral Obligation
Yoon, C. (2011). Theory of planned behavior and ethics theory in digital piracy: An integrated model. Journal of Business Ethics, 100(3), 405-417. Goles, T., Jayatilaka, B., George, B., Parsons, L., Chambers, V., Taylor, D., & Brune, R. (2008). Softlifting: Exploring Determinants of Attitude. Journal of Business Ethics, 77(4), 481-499. doi:10.1007/s10551-007-9361-0 Cronan, T., & Al-Rafee, S. (2008). Factors that Influence the Intention to Pirate Software and Media. Journal of Business Ethics, 78(4), 527-545. doi:10.1007/s10551-007-9366-8 Attitude towards piracy
Al-Rafee, S., & Cronan, T. P. (2006). Digital piracy: Factors that influence attitude toward behavior. Journal of Business Ethics, 63(3), 237-259. Cronan, T. P., & Al-Rafee, S. (2008). Factors that influence the intention to pirate software and media. Journal of Business Ethics, 78(4), 527-545. Goles, T., Jayatilaka, B., George, B., Parsons, L., Chambers, V., Taylor, D., & Brune, R. (2008). Softlifting: exploring determinants of attitude. Journal of Business Ethics, 77(4), 481-499.
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Perceived Risk
Liao, C., Lin, H. N., & Liu, Y. P. (2010). Predicting the use of pirated software: A contingency model integrating perceived risk with the theory of planned behavior. Journal of Business Ethics, 91(2), 237252. Social-Desirability Scale
Farrell, M. A., & Oczkowski, E. (2012). Organisational identification and leader member exchange influences on customer orientation and organisational citizenship behaviors. Journal of Strategic Marketing, 20(4), 365-377.
Questionnaire Wording In some cases, questions that seemed alike but were worded differently were used. This was used as a screening technique to filter out those respondents who had not filled the questionnaire with the requisite seriousness or had misunderstood the meaning and intention of the questions. Also, care was taken that questions covering two separate issues were not merged into one.
Response Choices All the questions (except one asking for the roll number of the respondent) were closed-ended. The advantage of using closed-ended questions is that they are simpler for the respondent to answer and also easier to code and analyse. The scales or the number of response choices for each item were as per the Likert scales used in the standard questionnaires. It was ensured that the response choices were exhaustive and mutually exclusive. This includes non-substantive choices like ‘neither agree nor disagree’ etc. Also, ranges were used instead of exact values for certain questions, especially where personal data was being asked for as these questions are seen as sensitive by the respondents.
Question Sequence The questionnaire was divided into two parts: the main body and respondent characteristics. The part about the respondent characteristics was intentionally kept at the end so that respondents would not abandon the questionnaire in the middle of the survey for fear of disclosing sensitive information. In fact, as per the paper by Nicolaos E. Synodinos, if sensitive questions are kept towards the end, they result in higher response rates. Care was also taken that items measuring a single construct were grouped together.
Questionnaire Pretesting Once the questionnaire was developed, it was pretested by the members of another group. Pretesting is normally done to refine the questionnaire and the administration method. But in our case, the pretesting did not generate any negative response. Hence, no iteration was done. The same questionnaire was used as the final version.
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Administering the Questionnaire - Sample Design and Response Rate Having developed the questionnaire, the next step involved selecting the respondents and deciding the questionnaire administration method. Since the topic of the research was to determine the factors that affect the intention to commit digital piracy, the questionnaire was floated mainly to students studying in the various B-schools across the country as the frequency of internet usage among them is very high. Self-administered questionnaires were used for the survey. The questionnaires were developed using Qualtrics software and sent to the respondents via e-mail. This method of administering the questionnaire helped in reaching a larger pool of respondents. Also, it provided the respondents with sufficient time to fill the questionnaires. The questionnaires were sent to 185 students and the number of responses obtained was 153. This resulted in a response rate of 82.7%.
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Analysis and Discussion of Results Data Analysis forms the crux of the research as analysis of the responses received through our survey will reveal the findings of our study and possible implications for future. Data analysis will also help us to assess validity of our hypothesis and will uncover any research gaps that may exist for future researchers to study. The data analysis for this research includes a sequence of steps which are to be performed one after the other. One can only move to the next step if it passes the previous one. Output of each stage represent one or more characteristics of the sample response data. The steps which have been followed are described in the following section. 1. Skewness and Kurtosis In statistics, Skewness is a measure of the asymmetry of the probability distribution of a realvalued random variable about its mean. It quantifies how symmetrical the distribution is. It can take the following three kinds of values.
Skewness > 0 - Right skewed distribution - most values are concentrated on left of the mean, with extreme values to the right. Skewness < 0 - Left skewed distribution - most values are concentrated on the right of the mean, with extreme values to the left. Skewness = 0 - mean = median, the distribution is symmetrical around the mean.
In statistics, Kurtosis (from the Greek word kyrtos or kurtos, meaning curved, arching) is any measure of the peakedness of the probability distribution of a real-valued random variable. It quantifies whether the shape of the data distribution matches the Gaussian distribution. It measures the height and sharpness of the peak relative to the rest of the data. Higher values indicate a higher, sharper peak; lower values indicate a lower, less distinct peak. Various values of kurtosis signify different characteristics of data.
Kurtosis > 3 - Leptokurtic distribution, sharper than a Gaussian distribution, with values concentrated around the mean and thicker tails. This means high probability for extreme values. Kurtosis < 3 - Platykurtic distribution, flatter than a Gaussian distribution with a wider peak. The probability for extreme values is less than for a Gaussian distribution, and the values are wider spread around the mean. Kurtosis = 3 - Mesokurtic distribution - Gaussian distribution for example.
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The two values for all the responses to each of our question are listed in the following tables. Item att1 att2 att3 att4 att5 att6 att7 att8 att9 att10 att11 att12 att13 att14 att15 att16 risk1 risk2 risk3 risk4 risk5 risk6 risk7 risk8 risk9 risk10 pbc1 pbc2 pbc3 pbc4 pbc5 mo1 mo2 mo3 mo4 mo5 mo6 mo7 mo8 mo9 sn1
Skewness Std. Error of Skewness -.145 .196 -.024 .196 -.21 .196 -.077 .196 .157 .196 .073 .196 -.085 .196 .045 .196 .131 .196 .048 .196 .15 .196 .35 .196 .374 .196 .14 .196 .171 .196 -.007 .196 -.074 .196 .083 .196 .150 .196 .308 .196 .437 .196 .375 .196 -.084 .196 .248 .196 .310 .196 .387 .196 .257 .196 .403 .196 .340 .196 .354 .196 .545 .196 .233 .196 .098 .196 .042 .196 .304 .196 -.198 .196 .214 .196 .078 .196 .233 .196 .019 .196 -.323 .196
Kurtosis -.791 -.907 -.622 -.671 -1.105 -.987 -.976 -.887 -.875 -.899 -.845 -.827 -.963 -1.042 -1.15 -.872 -.624 -.448 -.608 -.717 -.421 -.663 -1.005 -.745 -.608 -.506 -1.010 -.894 -.951 -.911 -.857 -1.141 -1.138 -1.115 -.685 -1.185 -1.202 -1.127 -.885 -1.141 -.907
Std. Error of Kurtosis .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .39 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390
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sn2 sn3 sn4 sn5 sn6 sn7 sn8 sn9 sn10 sn11 int1 int2 int3 int4 int5 ce1 ce2 ce3 ce4 ce5 ce6 ce7 ce8 ce9 ce10 sdb1 sdb2 sdb3 sdb4 sdb5 sdb6 college gender age workex freq dcat
-.099 -.054 -.223 -.348 -.213 -.176 .245 .218 .470 .681 .166 .081 .290 .532 .092 -.261 -.292 -.378 -.529 -.547 -.530 -.629 -.557 -.694 -.662 -.053 -.269 -.358 -.316 .189 -.289 -2.149 .913 1.222 .290 .762 .662
.196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .302 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196 .196
-1.019 .333 -.504 -.526 -.872 -.777 -.845 -.746 -.791 -.383 -.919 -1.041 -.945 -1.739 -.701 -.851 -.824 -.736 -.828 -.685 -.637 -.399 -.461 -.519 -.541 -1.253 -.970 -.654 -.544 -.842 -.701 2.801 -1.183 .027 -1.057 -.690 -.221
.390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .595 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390 .390
Interpretation Almost all the items have skewness values as close to zero, therefore the response data for them is fairly distributed and not skewed to any particular direction. However for items like college and age, the skewness values are close to -2 and +1 respectively, thereby suggesting that their
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responses are skewed in one particular direction. The possible explanation is that most of the respondents were from the same college and are in their mid-twenties. Kurtosis values for all items lie within -2 and +3. Therefore the data is neither leptokurtic nor platykurtic. Hence the histogram of this data will neither be too sharper nor too flatter when compared to a Gaussian distribution. In a liberal approach, the two values must lie between -12 and +12. Clearly, our data passes this test and we move to the next step.
2. Reverse Code Negatively worded items 15 items were identified as negatively worded and reverse coded using “Compute Variable” instead of “Recode into different variable” method. The two methods produce same result when the scale of the item is a Likert scale (an arithmetic progression is required). The details can be found out in Appendix-1.
3. Factor Analysis Factor analysis is a statistical method used to study the dimensionality of a set of variables. In factor analysis, latent variables represent unobserved constructs and are referred to as factors or dimensions.
Exploratory Factor Analysis (EFA) Used to explore the dimensionality of a measurement instrument by finding the smallest number of interpretable factors needed to explain the correlations among a set of variables – exploratory in the sense that it places no structure on the linear relationships between the observed variables and on the linear relationships between the observed variables and the factors but only specifies the number of latent variables.
Confirmatory Factor Analysis (CFA) Used to study how well a hypothesized factor model fits a new sample from the same population or a sample from a different population – characterized by allowing restrictions on the parameters of the model.
The output of this analysis produces three important statistic. A. Kaiser-Meyer-Olkin (KMO): It compares the magnitude of observed correlation coefficients with the magnitudes of partial correlation coefficients. Its value must be at least 0.5 to proceed to the next step.
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B. Communality: The sum of the squared factor loadings for all factors for a given variable (row) is the variance in that variable accounted for by all the factors, and this is called the communality. The communality measures the percent of variance in a given variable explained by all the factors jointly and may be interpreted as the reliability of the indicator. It must be greater than 0.3. C. Eigen Values: The eigenvalue for a given factor measures the variance in all the variables which is accounted for by that factor. Eigenvalues measure the amount of variation in the total sample accounted for by each factor. It must be greater than 1. D. Factor Loadings: The factor loadings, also called component loadings are the correlation coefficients between the cases (rows) and factors (columns). It must be at least 0.4 for each item. Construct-wise Factor analysis results for the respondent data for our research are given below: 1) Attitude KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square df Sig.
att1 att2 att3 att4 att5 att6 att7 att8 att9 att10 att11r att12r att13r att14r att15r
Communalities Initial Extraction 1.000 .784 1.000 .815 1.000 .797 1.000 .743 1.000 .842 1.000 .849 1.000 .824 1.000 .858 1.000 .866 1.000 .813 1.000 .827 1.000 .901 1.000 .877 1.000 .561 1.000 .576
.881 2286.813 105 .000
Rotated Component Matrixa Component 1 2 3 att1 .830 att2 .851 att3 .855 att4 .784 att5 .867 att6 .884 att7 .855 att8 .866 att9 .867 att10 .820 att11r .901 att12r .947 att13r .934 att14r .666 att15r .718
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Item att16r was found to have a communality of 0.05) are shown in Red. The R2 value of .415 shown in the Model summary table imply 41.5% impact of the independent variables on the dependent variable. The coefficient table gives each independent variable’s individual coefficient in the regression equation.
Regression Analysis to measure impact of: Encouraging Attitude (Independent) Overall Attitude (Independent) Adverse Attitude (Independent) Perceived Psychosocial risk (Independent) Perceived Functional risk (Independent) Perceived Behavioral Control (Independent) Music Piracy Subjective Norms (Independent) Digital Piracy Subjective Norms (Independent) on
Intention to commit Digital Piracy (Dependent)
Model Summary Model
R .778a
1
R Square
Adjusted R Square
.605
.583
Std. Error of the Estimate 3.93360
a. Predictors: (Constant), DPSN_tot, AA_tot, PBC_tot, MPSN_tot, PFR_tot, EA_tot, PPSR_tot, OA_tot
ANOVAa Model 1
Sum of Squares
df
Mean Square
F
Sig.
Regression
3417.634
8
427.204
27.609
.000b
Residual
2228.144
144
15.473
Total
5645.778
152
a. Dependent Variable: Int_tot b. Predictors: (Constant), DPSN_tot, AA_tot, PBC_tot, MPSN_tot, PFR_tot, EA_tot, PPSR_tot, OA_tot
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Coefficientsa Model
1
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
1.378
.170
B
Std. Error
Beta
(Constant)
5.191
3.768
EA_tot
.084
.051
.124
1.638
.104
OA_tot
.248
.069
.321
3.598
.000
AA_tot
-.017
.072
-.013
-.237
.813
PPSR_tot
.294
.151
.151
1.942
.054
PFR_tot
-.023
.171
-.009
-.132
.895
PBC_tot
-.400
.057
-.482
-6.975
.000
MPSN_tot
.053
.085
.041
.625
.533
DPSN_tot
.085
.074
.076
1.138
.257
a. Dependent Variable: Int_tot
The items which are significant are shown in Green whereas the items which are not significant (Sig > 0.05) are shown in Red. The R2 value of .605 shown in the Model summary table imply 60.5% impact of the independent variables on the dependent variable. The coefficient table gives each independent variable’s individual coefficient in the regression equation.
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Final Research Model
Attitude Encouraging Attitude to commit Piracy
Subjective Norms Music Piracy Subjective Norms
Overall Attitude to commit Piracy
Digital Piracy Subjective Norms Adverse Attitude to commit Piracy
Intention to Commit Digital Piracy Perceived Risk
Perceived Behavioral Control
Perceived Psycho-social Risk Perceived Functional Risk
Moral Obligation
Computer Efficacy
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Implications of the Study Findings We started out by trying to measure what factors influence an individual’s intention to commit digital piracy. The research methodology followed was a survey and analysis of the data to relate various constructs derived it to the intention to commit digital piracy. The major variables which were found to have a significant impact on the intention to commit digital piracy are: 1. Overall Attitude towards Digital Piracy The overall attitude towards digital piracy is a dimension of attitude towards digital piracy. This construct was created after factor analysis of attitude towards digital piracy. This construct contained questions which pertained to the general attitude of an individual towards digital piracy. This construct is thus important to measure how any individual sees digital piracy and thus has an affect towards piracy. Attitude as defined by Carl Jung (Jung, 1921) is a “readiness of the psyche to act or react in a certain way”. Attitude can be classified into explicit or implicit and it is formed over a period of time due to past experiences. The ways in which attitude impacts actual action/behavior was propounded by Fishben and Icek, (1975, 1980) in the theory of reasoned action. This shows that any changes in attitude can lead to a corresponding change in behavioral intentions. From our survey analysis results, we too can conclude that there is a significant impact of overall attitude towards digital piracy on intention to commit digital piracy. 2. Perceived Behavioral Control Perceived behavioral control is an individual's perceived ease or difficulty of performing the particular behavior (Ajzen, 1991). It is assumed that perceived behavioral control is determined by the total set of accessible control beliefs. Control beliefs are an individual's beliefs about the presence of factors that may facilitate or impede performance of the behavior (Ajzen, 2001). The concept of perceived behavioral control is conceptually related to self-efficacy. Now in our model perceived behavioral control is again impacted by moral obligation and computer efficacy. This will help narrow the specifics of perceived behavioral control to the scope of our research. From the survey analysis and results, we can conclude that both moral obligation and computer efficacy have significant impact on perceived behavioral control. Perceived behavioral control in turn has a significant impact on the intention to commit digital piracy. As we have proved that these constructs are significant, we can now evaluate as to how to manipulate them to ensure reduction in the intention to commit digital piracy. 3. Perceived Psychosocial risk The perceived psycho-social risk is a dimension of perceived risk. This construct was created after factor analysis of perceived risk. The actual psychosocial risk is defined by the risk which a person experiences when the uncertainty of any situation he/she is in may have uncertain 37 | P a g e
outcomes on the psychological or social ends. The perceived psychosocial risk is a combination of social and psychological risk as proposed by Jacoby and Kaplan (1972). As per our model, this perceived psychosocial risk is a characteristic in which individuals will be concerned about what they themselves and their immediate peers/loved ones will think of them in case they are exposed to be indulging in digital piracy. This perception of risk, if found to be high, inhibits most people from indulging in digital piracy. Thus according to our results, people will be likely to change their intent to commit digital piracy if there is a change in their overall attitude to digital piracy, their moral obligation and their perceived psychosocial risk from piracy. To combat piracy, two popular methods have been employed: preventives and deterrents. Preventives impede the act of piracy by making it very hard to do so. The idea is to make the pirates expend so much effort that it will wear them down, and eventually they will not want to do it. Deterrents, on the other hand, use the threat of undesirable consequences (mostly legal sanctions) to prevent piracy (Gopal and Sanders, 1997). Unfortunately, none of these strategies seem to be working; this is evident by amount of losses published by the Business Software Alliance (BSA) in the last few years (the Asia/Pacific area had piracy loses increase from $2.7 Billion in 1998 to $4.7 Billion in 2001 according to the BSA) and the expected increases in non-software piracy. Instead of relying solely on preventives and deterrents, knowing what might influence individuals to pirate would be a more advantageous path. The most salient belief within cognitive beliefs was that subjects believed that they could save money by pirating digital media. Another related and significant salient belief was that subjects believed that digital media is overpriced. There has been a move recently to lower the price of digital media to curb piracy. By lowering the prices, digital pirates will reexamine the cost of pirating versus buying and hopefully tilt the balance towards buying versus pirating (Cheng et al., 1997). Another avenue that might also be worthwhile pursuing is to better educate the public on why these prices should be the way they are (by explaining the different costs associated with making/promoting digital media). First, this study reveals that moral obligation is one of the most influential factor on intention to commit digital piracy. According to Reidenbach and Robin (1990), a popular understanding of these normative beliefs comes to the general public. Therefore, it is desirable to enlighten people and imprint into their minds the fact that pirating is morally wrong and it is bad behavior through advertisements in the mass media, such as via TV broadcasting. Second, perceived behavioral control is a more influential factor in pirating digital materials. It means that individuals who have the skills and resources to pirate digital materials have a higher intention of pirate digital materials. Therefore, in order to make pirating a much more difficult thing to accomplish for such people, software and digital media industries should use technologies actively to secure their digital materials, such as DRM (digital rights management). Thirdly, overall attitude were also found to affect intention to commit digital piracy. This result verifies the fact that the importance of attitude on actual behavior may vary depending on how long an individual has been pirating software (Limayem et al., 2004). In order to break the habit of digital
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pirating, it is desirable to enforce copyright laws and to increase individuals’ awareness of the potential severity and certainty of punishment. Finally, the above point and associated awareness among people that piracy is bad for the society’s welfare will increase the perceived psychosocial risk among individuals. Thus, their overall intention to commit digital piracy will come down significantly and this will lead to alleviation in the global problem of digital piracy. Now looking at the implications for a B-School where we saw intention to commit digital piracy as a surrogate measure of ethical behavior. We find that since the actual intention to commit is moderate (11/28 median) along with high computer efficacy scores of 69 (median) out of 100, we feel that there is a moderate intention to commit digital piracy due to higher perceived behavioral control (median is 17 out of max 35) due to moral obligation scores being higher (median is 26 out of max 49). Thus there is moderate possibility that the B-school will be showing some unethical behavior in their lifetimes. However, piracy behavior can be curbed even in campuses by implementing the above suggestions.
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Appendix Appendix-1 Reverse Coded Items
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Appendix-2 Computer Efficacy items recoded to ceXXrecoded Rule: Old Value=11, New Value=0; ELSE Copy
Appendix-3 Int5 recoded to int5Recode and sum calculated for Regression Rule: Old Value=SYSMIS, New Value=0; Old Value=MISSING, New Value=0; ELSE, Copy
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Appendix-4 Sum of constructs after Reliability Analysis
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Appendix-5 Questionnaire Attitude towards piracy (7 point Likert scale) 1. Overall attitude towards digital piracy (Unfavorable……………………………….Favorable) 2. Overall attitude towards digital piracy (Harmful………………………………………….Beneficial) 3. Overall attitude towards digital piracy (Foolish………………………………..Wise) 4. Overall attitude towards digital piracy (Bad……………………………Good) 5. I feel elated when I pirate digital material (Not at all……………………………… Very much so) 6. I feel excited when I pirate digital material (Not at all……………………………… Very much so) 7. I feel active when I pirate digital material (Not at all……………………………… Very much so) 8. I feel happy when I pirate digital material (Not at all……………………………… Very much so) 9. I feel pleased when I pirate digital material (Not at all……………………………… Very much so) 10. I feel satisfied when I pirate digital material (Not at all……………………………… Very much so) 11. I feel anxious when I pirate digital material (Not at all……………………………… Very much so) 12. I feel fearful when I pirate digital material (Not at all……………………………… Very much so) 13. I feel nervous when I pirate digital material (Not at all……………………………… Very much so) 14. Using copied software is a bad idea (Strongly disagree …………….. strongly agree=7) 15. I dislike the idea of using copied software (Strongly disagree …………….. strongly agree=7) 16. Using copied software is a wise idea (Strongly disagree …………….. strongly agree=7) Perceived Risk (5 point Likert scale) 1. What is the probability that pirated software will fail to work like the original one? (very low/very high) 2. What is the probability that pirated software will malfunction or damage your computer system? (very low/very high) 3. What is the probability that pirated software will fail to function? (very low/very high) 4. If your friends, relatives or associates are aware that you have used pirated software, what is the probability that they will look down on you because they think that you cannot afford original software? (very low/very high) 5. If your friends, relatives or associates are aware that you have used pirated software, what is the probability that you will lose their respect because they will regard you as unethical? (very low/very high) 6. If you have used pirated software, what is the probability that you will be caught for the infringement of copy right law? (very low/very high) 7. You may be arrested for infringement of copyright law if you have used pirated software, (strongly disagree/strongly agree) 8. Using pirated software makes me feel psychologically uncomfortable, (strongly disagree/strongly agree) 9. Using pirated software gives me a feeling of unwanted anxiety, (strongly dis agree/strongly agree) 10. Using pirated software causes me to experience unnecessary tension, (strongly dis agree/strongly agree) 46 | P a g e
Perceived behavioral control (7 point Likert scale) 1. 2. 3. 4.
For me to pirate digital material, it would be (Very Easy to Very Difficult) If I wanted to, I could easily pirate digital material (Strongly Agree to Strongly Disagree) I believe that I have the ability to pirate digital material (Strongly Agree to Strongly Disagree) I have the resources necessary to pirate digital material (Strongly Agree to Strongly Disagree) 5. I can find digital material to pirate if I wanted to (Strongly Agree to Strongly Disagree)
Moral Obligation (7 point Likert scale) 1. I would feel guilty if I pirated digital products (Strongly disagree/ strongly agree) 2. To pirate digital products goes against my principles (Strongly disagree/ strongly agree) 3. It would be morally wrong for me to pirate digital products (Strongly disagree/ strongly agree) 4. It is my obligation as a personal computer user not to copy software (Strongly disagree/ strongly agree) 5. I would not feel guilty if I used copied software (Strongly disagree/ strongly agree) 6. I would feel guilty if I copied software (Strongly disagree/ strongly agree) 7. I would not feel guilty if I pirated digital material (Strongly disagree/ strongly agree) 8. Digital piracy goes against my principles (Strongly disagree/ strongly agree) 9. It would be morally wrong to pirate digital material (Strongly disagree/ strongly agree)
Subjective Norms (7 point Likert scale) 1. Most people who are important to me think I should not pirate digital material (Strongly Agree/ Strongly Disagree) 2. When considering digital piracy, I wish to do what people who are important to me want me to do (Strongly Agree/ Strongly Disagree) 3. If I pirate digital material, then most people who are important to me would (Not Care/ Disapprove) 4. If I pirated digital products, most of the people who are important to me would disapprove (Strongly agree/ strongly disagree) 5. Most people who are important to me would look down on me if I pirated digital products (Strongly agree/ strongly disagree) 6. No one who is important to me thinks it is okay to commit digital piracy (Strongly agree/ strongly disagree) 7. My colleagues think digital piracy behavior is wrong (Strongly agree/ strongly disagree) 8. Most people who are important to me think that I should download music. (Strongly agree/ strongly disagree) 9. The people in my life whose opinions I value would think that I should download music (Strongly agree/ strongly disagree) 47 | P a g e
10. Most people who are important to me often download music (Strongly agree/ strongly disagree) 11. The people in my life whose opinions I value often download music (Strongly agree/ strongly disagree) Intention to commit digital piracy (7 point Likert scale except Q4) 1. I intend to pirate digital material in the near future (Definitely do not to Definitely do) 2. I will try to pirate digital material in the near future (Definitely will not to Definitely will) 3. I will make an effort to pirate digital material in the near future (Definitely False to Definitely True) 4. I have pirated digital material in the past (Yes and No) 5. How much digital material did you pirate? (skip if you answered no in the last one) (Very Little to A lot) Social-Desirability Scale (5 point Likert scale) 1. 2. 3. 4. 5. 6.
There have been occasions when I took advantage of someone. I sometimes try to get even rather than forgive and forget. At times I have really insisted on having things my own way. I like to gossip at times. I have never deliberately said something that hurt someone’s feelings. I’m always willing to admit when I make a mistake.
Computer Efficacy (10 point Likert scale) Often in our jobs we are told about software packages that are available to make work easier. For the following questions, imagine that you were given a new software package for some aspect of your work. It doesn't matter specifically what this software package does, only that it is intended to make your job easier and that you have never used it before I could complete the job using the software package... 1. ...if there was no one around to tell me what to do as I go. 2. ...if I had never used a package like it before. 3. ...if I had only the software manuals for reference. 4. ...if I had seen someone else using it before trying it myself. 5. ...if I could call someone for help if I got stuck. 6. ...if someone else had helped me get started. 7. ...if I had a lot of time to complete the job for which the software was provided. 8. ...if I had just the built-in help facility for assistance. 9. ...if someone showed me how to do it first. 10. ...if I had used similar packages before this one to do the same job.
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Demographic Questions College (If XLRI, mandatory Roll No, otherwise optional Roll No) Age (30) Gender Download frequency per month (0-50 times, 51-100 times, 100-200 times, >200 times) Major Download Category (Educational Materials, Movies, Songs, Software, Games, Others) Years of full-time work experience (3)
Qualtrics Credentials Username:
[email protected] Password: aaaaa12359 Survey Name: CompleteSurvey v2
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