SRM HRM A Group No. 01 Project Report

A Quantitative Research to

Determine the Factors Affecting

Intention to Commit Digital Piracy

Social Research Methods

HRM Section A - Group 1

Aaditya Narayan Chaudhary H13001 Aayush Goel H13002 Abhay Kumar Vasishtha H13003 Abhishek Saxena H13005 Akshay Maxim Sequeira H13009

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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 & Al-

Rafee (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 multi-

dimensional 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).


Fast Internet connections, availability of inexpensive high capacity storage, and underground peer-

to-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 self-

efficacy 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

-

-

- -

-

-

Encouraging Attitude to commit Piracy

Overall Attitude to commit Piracy

Adverse Attitude to commit Piracy

Attitude

Perceived Psycho-social Risk

Perceived Functional Risk

Perceived Risk

Music Piracy Subjective Norms

Digital Piracy Subjective Norms

Subjective Norms


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.


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), 237-

252.

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 real-valued 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 Skewness Std. Error of Skewness Kurtosis Std. Error of Kurtosis

att1 -.145 .196 -.791 .39

att2 -.024 .196 -.907 .39

att3 -.21 .196 -.622 .39

att4 -.077 .196 -.671 .39

att5 .157 .196 -1.105 .39

att6 .073 .196 -.987 .39

att7 -.085 .196 -.976 .39

att8 .045 .196 -.887 .39

att9 .131 .196 -.875 .39

att10 .048 .196 -.899 .39

att11 .15 .196 -.845 .39

att12 .35 .196 -.827 .39

att13 .374 .196 -.963 .39

att14 .14 .196 -1.042 .39

att15 .171 .196 -1.15 .39

att16 -.007 .196 -.872 .39

risk1 -.074 .196 -.624 .390

risk2 .083 .196 -.448 .390

risk3 .150 .196 -.608 .390

risk4 .308 .196 -.717 .390

risk5 .437 .196 -.421 .390

risk6 .375 .196 -.663 .390

risk7 -.084 .196 -1.005 .390

risk8 .248 .196 -.745 .390

risk9 .310 .196 -.608 .390

risk10 .387 .196 -.506 .390

pbc1 .257 .196 -1.010 .390

pbc2 .403 .196 -.894 .390

pbc3 .340 .196 -.951 .390

pbc4 .354 .196 -.911 .390

pbc5 .545 .196 -.857 .390

mo1 .233 .196 -1.141 .390

mo2 .098 .196 -1.138 .390

mo3 .042 .196 -1.115 .390

mo4 .304 .196 -.685 .390

mo5 -.198 .196 -1.185 .390

mo6 .214 .196 -1.202 .390

mo7 .078 .196 -1.127 .390

mo8 .233 .196 -.885 .390

mo9 .019 .196 -1.141 .390

sn1 -.323 .196 -.907 .390

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sn2 -.099 .196 -1.019 .390

sn3 -.054 .196 .333 .390

sn4 -.223 .196 -.504 .390

sn5 -.348 .196 -.526 .390

sn6 -.213 .196 -.872 .390

sn7 -.176 .196 -.777 .390

sn8 .245 .196 -.845 .390

sn9 .218 .196 -.746 .390

sn10 .470 .196 -.791 .390

sn11 .681 .196 -.383 .390

int1 .166 .196 -.919 .390

int2 .081 .196 -1.041 .390

int3 .290 .196 -.945 .390

int4 .532 .196 -1.739 .390

int5 .092 .302 -.701 .595

ce1 -.261 .196 -.851 .390

ce2 -.292 .196 -.824 .390

ce3 -.378 .196 -.736 .390

ce4 -.529 .196 -.828 .390

ce5 -.547 .196 -.685 .390

ce6 -.530 .196 -.637 .390

ce7 -.629 .196 -.399 .390

ce8 -.557 .196 -.461 .390

ce9 -.694 .196 -.519 .390

ce10 -.662 .196 -.541 .390

sdb1 -.053 .196 -1.253 .390

sdb2 -.269 .196 -.970 .390

sdb3 -.358 .196 -.654 .390

sdb4 -.316 .196 -.544 .390

sdb5 .189 .196 -.842 .390

sdb6 -.289 .196 -.701 .390

college -2.149 .196 2.801 .390

gender .913 .196 -1.183 .390

age 1.222 .196 .027 .390

workex .290 .196 -1.057 .390

freq .762 .196 -.690 .390

dcat .662 .196 -.221 .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

21 | P a g e

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. .881

Bartlett's Test of Sphericity Approx. Chi-Square 2286.813

df 105

Sig. .000

Communalities

Initial Extraction

att1 1.000 .784

att2 1.000 .815

att3 1.000 .797

att4 1.000 .743

att5 1.000 .842

att6 1.000 .849

att7 1.000 .824

att8 1.000 .858

att9 1.000 .866

att10 1.000 .813

att11r 1.000 .827

att12r 1.000 .901

att13r 1.000 .877

att14r 1.000 .561

att15r 1.000 .576

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 <0.3 and hence was removed before the 2nd iteration. All loadings found were clear loadings. The three components identified are Encouraging Attitude, Overall Attitude and Adverse Attitude.

2) Perceived Risk

Item risk7 was found to be mis-loaded and was removed before the 2nd iteration. The components identified are Perceived Psychosocial Risk and Perceived Functional Risk.

3) Perceived Behavioral Control

Communalities

Initial Extraction

risk1 1.000 .780

risk2 1.000 .714

risk3 1.000 .803

risk4 1.000 .733

risk5 1.000 .792

risk6 1.000 .607

risk8 1.000 .581


Component

1 2

risk1 .865

risk2 .779

risk3 .882

risk4 .835

risk5 .883

risk6 .747

risk8 .683




df 21

Sig. .000




df 10

Sig. .000

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All loadings found were clear loadings. The single component identified is Perceived Behavioral Control.

4) Moral Obligation

Item mo5r and mo7r were found to be mis-loaded and were removed before subsequent iterations. The single component identified is Moral Obligation.

Communalities

Initial Extraction

pbc1 1.000 .785

pbc2 1.000 .813

pbc3 1.000 .844

pbc4 1.000 .798

pbc5 1.000 .673


Component

1

pbc1 .886

pbc2 .901

pbc3 .919

pbc4 .893

pbc5 .820

Communalities

Initial Extraction

mo1 1.000 .704

mo2 1.000 .814

mo3 1.000 .786

mo4 1.000 .666

mo6 1.000 .692

mo8 1.000 .787

mo9 1.000 .776


Component

1

mo1 .839

mo2 .902

mo3 .887

mo4 .816

mo6 .832

mo8 .887

mo9 .881




df 21

Sig. .000

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5) Subjective Norms

Item sn6 and sn3r were found to be cross-loaded and single-loaded respectively and were removed in subsequent iterations. The two components identified are Music Piracy Subjective Norms and Digital Piracy Subjective Norms.

6) Computer Efficacy

Communalities

Initial Extraction

sn1 1.000 .741

sn2 1.000 .494

sn4 1.000 .695

sn5 1.000 .673

sn7 1.000 .519

sn8r 1.000 .767

sn9r 1.000 .710

sn10r 1.000 .741

sn11r 1.000 .743


Component

1 2

sn1 .840

sn2 .696

sn4 .792

sn5 .787

sn7 .683

sn8r .861

sn9r .825

sn10r .847

sn11r .851




df 36

Sig. .000




df 45

Sig. .000

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All loadings found were clear loadings. The single component identified is Computer Efficacy.

7) Intention to Commit Digital Piracy

All loadings found were clear loadings. The single component identified is Intention to

Commit Digital Piracy.

Hence, after Factor Loading the components identified are:

I. Encouraging Attitude (Independent)

II. Overall Attitude (Independent)

III. Adverse Attitude (Independent)

Communalities

Initial Extraction

ce1recode 1.000 .661









ce10recode 1.000 .713


Component

1

ce1recode .813

ce2recode .657

ce3recode .845

ce4recode .918

ce5recode .904

ce6recode .917

ce7recode .899

ce8recode .849

ce9recode .870

ce10recode .844

Communalities

Initial Extraction

int1 1.000 .840

int2 1.000 .855

int3 1.000 .863

int5Reocode 1.000 .556


Component

1

int1 .917

int2 .925

int3 .929

int5Reocode .745




df 6

Sig. .000

27 | P a g e

IV. Perceived Psychosocial risk (Independent)

V. Perceived Functional risk (Independent)

VI. Perceived Behavioral Control (Intermediate)

VII. Moral Obligation (Independent)

VIII. Music Piracy Subjective Norms (Independent)

IX. Digital Piracy Subjective Norms (Independent)

X. Computer Efficacy (Independent)

XI. Intention to commit Digital Piracy (Dependent)

4. Reliability Analysis

Reliability is concerned with consistency, accuracy and predictability of the scale. It refers to the

extent to which a measurement process is free from random errors. It also measures the fact that

a scale should consistently reflect the construct. The split-half reliability method is used to assess

reliability of a dataset. The method uses Cronbach alpha coefficient () to measure reliability of

the dataset. Its value must be at least 0.7. In addition to this, only items with value of Corrected

Item-Total Correlation (CITC) at least 0.4 must be considered.

Reliability analysis for each component is as follows:

1) Encouraging Attitude

Reliability Statistics

Cronbach's Alpha N of Items

.961 6

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

att5 18.05 56.886 .875 .954

att6 17.78 55.460 .878 .954

att7 17.69 57.188 .867 .955

att8 17.77 56.112 .891 .952

att9 17.84 57.072 .897 .952

att10 17.57 55.971 .855 .956

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2) Overall Attitude

3) Adverse Attitude



.914 6


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

att1 20.7320 42.974 .804 .892

att2 20.6797 42.588 .834 .888

att3 20.6078 44.095 .816 .891

att4 20.7190 44.940 .776 .897

att14r 20.6405 45.482 .667 .912

att15r 20.5425 44.474 .672 .912



.925 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

att11r 9.3856 10.607 .809 .922

att12r 9.0654 9.851 .882 .863

att13r 9.0784 9.770 .853 .887

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4) Perceived Psychosocial risk

5) Perceived Functional Risk



.834 4


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

risk4 7.38 5.408 .704 .772

risk5 7.53 5.659 .751 .752

risk6 7.54 6.171 .610 .813

risk8 7.27 6.082 .597 .819



.843 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

risk1 5.78 3.056 .724 .768

risk2 5.82 3.041 .674 .815

risk3 5.88 2.886 .729 .761

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6) Perceived Behavioral Control

7) Moral Obligation



.930 5


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

pbc1 14.03 35.907 .815 .914

pbc2 13.97 34.466 .838 .909

pbc3 14.01 33.829 .865 .904

pbc4 13.88 34.828 .830 .911

pbc5 14.07 35.949 .731 .930



.943 7


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

mo1 22.71 64.972 .782 .937

mo2 22.50 63.581 .860 .930

mo3 22.39 63.529 .840 .931

mo4 22.63 66.538 .751 .939

mo6 22.65 65.085 .772 .938

mo8 22.56 64.670 .840 .932

mo9 22.37 64.814 .831 .932

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8) Music Piracy Subjective Norms

9) Digital Piracy Subjective Norms



.885 4


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

sn8r 14.1046 12.950 .762 .847

sn9r 14.1765 13.791 .715 .864

sn10r 13.9150 12.223 .748 .854

sn11r 13.8627 13.132 .777 .841



.835 5


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

sn1 17.60 18.241 .748 .768

sn2 17.64 21.298 .475 .846

sn4 17.34 19.371 .709 .782

sn5 17.54 19.447 .692 .786

sn7 17.30 20.264 .570 .820

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10) Computer Efficacy

11) Intention to commit Digital Piracy

None of the items in all 11 constructs were found to be unreliable.



.957 10


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

ce1recode 60.2614 395.839 .780 .954

ce2recode 60.4706 409.672 .605 .961

ce3recode 60.1111 392.560 .812 .953

ce4recode 59.7908 384.193 .889 .950

ce5recode 59.6863 390.546 .874 .950

ce6recode 59.6667 390.500 .887 .950

ce7recode 59.6405 392.705 .866 .951

ce8recode 59.6536 401.557 .806 .953

ce9recode 59.5556 389.025 .828 .952

ce10recode 59.5163 390.593 .800 .953



.879 4


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

int1 7.7974 21.018 .802 .820

int2 8.6078 25.279 .835 .839

int3 8.0523 20.208 .844 .802

int5Reocode 9.8758 20.438 .606 .920

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5. Regression Analysis

Regression analysis is a statistical process for estimating the relationships among variables. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes, the predictor, explanatory or regressor variables). Multiple regression also allows you to determine the overall fit (variance explained) of the model and the relative contribution of each of the predictors to the total variance explained. We have used multiple regression to find out overall impact of all independent variables on the dependent variable. Spss software also gives individual contribution of each variable in the overall impact. Before doing regression, we found out sum of reliable items for each construct so as to use those values for regression analysis.

Regression Analysis to measure impact of Moral Obligation and Computer Efficacy on Perceived Behavioral Control:

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .645a .415 .408 5.64185 a. Predictors: (Constant), CE_tot, MO_tot

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 3393.662 2 1696.831 53.30

8

.000b

Residual 4774.573 150 31.830

Total 8168.235 152

a. Dependent Variable: PBC_tot b. Predictors: (Constant), CE_tot, MO_tot

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 9.580 2.171 4.413 .000

MO_tot .456 .050 .581 9.103 .000

CE_tot -.061 .021 -.184 -2.883 .005 a. Dependent Variable: PBC_tot

34 | P a g e

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 .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 R Square Adjusted R Square Std. Error of the Estimate

1 .778a .605 .583 3.93360 a. Predictors: (Constant), DPSN_tot, AA_tot, PBC_tot, MPSN_tot, PFR_tot, EA_tot, PPSR_tot, OA_tot

ANOVAa

Model Sum of

Squares

df Mean

Square

F Sig.

1 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

35 | P a g e

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 5.191 3.768 1.378 .170

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.

36 | P a g e

Final Research Model

Encouraging Attitude to commit Piracy

Overall Attitude to commit Piracy

Adverse Attitude to commit Piracy

Attitude

Perceived Psycho-social Risk

Perceived Functional Risk

Perceived Risk

Music Piracy Subjective Norms

Digital Piracy Subjective Norms

Subjective Norms


Moral Obligation

Computer Efficacy


37 | P a g e

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

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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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50. http://www.ats.ucla.edu/stat/seminars/muthen_08/part2.pdf 51. http://en.wikipedia.org/wiki/Skewness 52. http://en.wikipedia.org/wiki/Kurtosis 53. https://statistics.laerd.com/spss-tutorials/multiple-regression-using-spss-statistics.php 54. http://en.wikipedia.org/wiki/Regression_analysis

http://www.ats.ucla.edu/stat/seminars/muthen_08/part2.pdf

http://en.wikipedia.org/wiki/Skewness

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http://en.wikipedia.org/wiki/Regression_analysis

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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)

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Perceived behavioral control (7 point Likert scale)

1. For me to pirate digital material, it would be (Very Easy to Very Difficult)

2. If I wanted to, I could easily pirate digital material (Strongly Agree to Strongly Disagree)

3. I believe that I have the ability to pirate digital material (Strongly Agree to Strongly Disagree)

4. 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)

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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. There have been occasions when I took advantage of someone.

2. I sometimes try to get even rather than forgive and forget.

3. At times I have really insisted on having things my own way.

4. I like to gossip at times.

5. I have never deliberately said something that hurt someone’s feelings.

6. 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 (<20, 21-25, 26-30, >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 (<1, 1-2, 2-3, >3)

Qualtrics Credentials

Username: [email protected]

Password: aaaaa12359

Survey Name: CompleteSurvey v2

mailto:[email protected]

https://s.qualtrics.com/ControlPanel/?T=3XwZSp#SV_07CUx6AIjxaYYZv

SRM HRM A Group No. 01 Project Report

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