MBA 512 – Business Research & Design

Jason Giomboni and John Mullisky

Electronic Media Piracy

Legal vs. illegal use of content

Agenda

Introduction Exploration Data Collection Analysis Conclusion

Introduction Media – any form of electronic content used for

entertainment or education. Piracy – illegal downloading of software content

without payment.

Why perform the survey? John and myself have decades of experience in

the IT field and wanted to determine the percentage of media actually paid for.

We feel that all electronic media should be paid for based on the amount of time, resources and creative ability put into its development.

COD Example

ExplorationHypothesis Statement : People with less experience in life

based on education or work experience are more willing to download media content without payment.

Research Question: Do people with less education and/or work experience

download more electronic media content without payment for the content?

Relevant Factors: Age Income Education Work Experience Download Tendencies

Data Collection Survey: 21 Questions

Demographic: Gender, Education, Income, Work Experience, Computer Skill Level

Content: Movies, Music, Television, Adult, Software, Games

Behavior: Payment, Penalties, Guilt Survey Format:

Multiple ChoiceSingle ResponseMultiple ResponsesPayment RangesText Fields

Data Collection Target Audience:

Users of electronic devices that download electronic content.

Sample Size: 50 respondents Sampling Plan:

Email coworkers, friends and familyFacebook – sent to our friends and asked them

to send to their friends (random sample). Summary of Respondents:

Sent over one hundred and fifty emails and Facebook requests

50 Responses

Data Collection

Method of Administration:Created online survey (form) through

Google DocsWe couldn’t use Best Buy because of

solicitation policy

Data Collection – Gender Breakdown

Male – 30 60%Female – 20 40%

Gender:

Data Collection - Education Breakdown

High School or Below - 7 14%Trade or Certificate -1 2%Associates -7 14%Bachelor -10 20%Graduate and Above -25 50%

Current level of education completed:

Data Collection - Income Breakdown

< $25,000 - 7 14%$25,001 to $45,000 -8 16%$45,001 to $60,000 -13 26%$60,001 to $75,000 -7 14%> $75,001 -15 30%

Income Level:

Level of computer/technology skills:

17

2

6

25

Level of computer skillsIntermediate to Advanced users make up a large ma-

jority of the sample

Advanced

Beginner

Expert

Intermediate

Focused on reaching users of technology so this sample re-flects the skill set of the popu-lation that are users of tech-nology

Data Collection – Computer Skill Level

Type of hardware used

The majority of the respondents used the most widely available tools to download content.

Type of content by gender

Gender Music Games Movies Adult Software Television

Female 17 7 5 0 7 5Male 27 13 14 9 18 12

Analysis Summary Probability – identify probabilities for illegal

content downloads for various user classifications

One sample t-test – Test our data against a hypothesized value to see if the data is a likely representation of population

Two sample t-test – Test to see if our hypothesis is correct

Regression analysis – if the model is valid and if a relation ship exist

Content UsersProbabilities – User will pay for their content We used a pivot table to group the data

to determine the probability 12 out of 50 respondents pay for all their

downloaded content – 12/50 = 24% 25 out of 50 respondents pay for some

or none of their content – 25/50 = 50%

Another question ask if the users always paid for content the total number of yes responses was 16 – not 12

Content UsersProbabilities – User will pay for their content 6 out of 20 female respondents pay for all their

downloaded content – 6/20 = 30% 8 out of 20 female respondents pay for some

or none of their content – 8/20 = 40%

Content UsersProbabilities – User will pay for their content 6 out of 30 male respondents pay for all their

downloaded content – 6/30 = 20% 17 out of 30 male respondents pay for some or

none of their content – 17/30 = 56.6%

Males are least likely to pay for all of their downloaded content and more

likely to not pay for their content

Total - All Total - Some/None

Male - All Male - Some/None

Female - All Female - Some/None

0

10

20

30

40

50

60

Probabilty of paying for content

Probabilty of paying for content

Probabilities for the level of experience users paying for content

Advanced - Pay

Advanced - No pay

Beginner - Pay

Beginner - No Pay

Expert - Pay Expert - No Pay

Intermediate - Pay

Intermediate - No Pay

0%

20%

40%

60%

80%

100%

120%

35%

65%

0%

100%

17%

83%

36%

64%

Probabilty of always paying for content

Probabilty

Beginner – not enough users to indicate this is trendAdvanced and Intermediate are compared about equalExpert – See an increase in not always paying – may know more ways of getting illegal content

Number of device users by number of users who pay for content Probabilities

MP3 – 34% Smart Phone –

32% Laptop/PC –

25% iPad – 0% Game Console –

27% DVD/BluRay –

40%MP3 Player Smart Phone Laptop/PC iPad Game

ConsoleDVD/Blu-Ray

Player

0

5

10

15

20

25

30

35

40

45

50

One Sample Proportion t-test Tested to see if illegal download lead to

future purchases – Asked this question explicitly in the survey.

One Sample Proportion t-test Tested to see if level of work experience

affected the rate of illegal download. greater then 5 years of experience vs less than 5 years of experience.

Two Sample proportion test Do people with advanced degrees less

likely to illegally download content?

Two Sample proportion test Does salary affect payment for

download?

Two Sample proportion test High frequency of download affect

payment?

Reason given for illegal downloading

Reason # of Respondents

Why pay for it when you can get it for free somewhere else?

15

Needed it for school/work

8

Cost of paying too high 22

Can't find store to purchase it

5

Regression Test – Multiple Variable Dependent variable – Do you always

pay for your downloaded media/content?

Independent variables – Current Income, Years of work experience, Education Level, Level of computer skills, Frequency of downloading

Regression Test – Multiple Variable

Analysis Conclusion Does piracy lead to future purchases of

content? Probabilities indicate yes greater then 60% of the time.

Does having work experience greater then five years affect how likely a person is to download content illegally? No – equally likely with limited and greater experience

Are people with advance degrees less likely to download content illegally? No – equally likely with lower and advanced education

Analysis Conclusion Does income affect payment for content?

No – equally likely with low and high income

Do high frequency users pay for less content? No – equally likely between low and high frequency down loaders

Regression – Model is not valid and cannot identify relationship between variables

Conclusion There is a trend not to pay for all the content a

person may download, with a majority of males only paying for some if the content

Counter this with the analyst indicating future purchases from originally pirated content

Did not identify any survey data or analysis indicating that different levels of salary, education or work experience has a strong relationship with predicting content piracy

Impact – We have to answer our hypothesis question with a NO People with less education, lower salary or work

experience do not download more content illegally than those with higher levels of salary, work experience or education

Survey Improvements Execution: Phrasing the questions with

the method of analysis in mind – result in cleaner data with less transformation

Improve on the ability to obtain results from target audience – relied on “pass it along” methods to ensure we achieved a broad based response pool

Identify industry data for comparison – we had a broad definition of content – prevented comparison to available data

Future Research Possibilities We gathered data on the price a respondent would

be willing to pay for the content they downloaded. Interesting to see if this could be used to predict prices for content.

Follow up study – approximately 2/3rd of respondents indicated that an illegal download would lead to future purchase from the content owner – if this could be verified it may provide a means of marketing your content and “legalizing” content download

Questions?