Hinduja and Ingram (2009)
Contents
Source Details
Hinduja and Ingram (2009) | |
Title: | Social learning theory and music piracy: The differential role of online and offline peer influences |
Author(s): | Hinduja, S., Ingram, J. R. |
Year: | 2009 |
Citation: | Hinduja, S., & Ingram, J. R. (2009). Social learning theory and music piracy: the differential role of online and offline peer influences. Criminal Justice Studies, 22(4), 405-420. |
Link(s): | Definitive |
Key Related Studies: | |
Discipline: | |
Linked by: | Hinduja and Higgins (2011), Ingram and Hinduja (2008), Lee, Yeop Paek and Fenoff (2018) |
About the Data | |
Data Description: | Dataset consists of questionnaires answered by a large and heterogeneous group of 2032 undergraduate students in 2003 at a large public Midwestern university. |
Data Type: | Primary data |
Secondary Data Sources: | |
Data Collection Methods: | |
Data Analysis Methods: | |
Industry(ies): | |
Country(ies): | |
Cross Country Study?: | No |
Comparative Study?: | No |
Literature review?: | No |
Government or policy study?: | No |
Time Period(s) of Collection: |
|
Funder(s): |
|
Abstract
Social learning theory has been proven to demonstrate much explanatory value in the study of software and music piracy that occurs over the Internet. As a multifaceted predictive framework, though, the salience of some of the theory’s individual components has yet to be empirically measured. Answering the call of recent research to do just that, the current study seeks to identify the differential impact of offline and online peer influences on participation in music piracy. Results based on data from a sample of approximately 2000 university students indicated real-life peers had the strongest effect, after controlling for individuals’ demographic characteristics and Internet capabilities. To a lesser degree, though, online peers and online media sources (e.g., chat rooms) were also found to significantly predict participation in music piracy. Suggestions for policy stemming from these findings are discussed in conclusion with the intent of sharpening response efforts to reduce intellectual property theft in cyberspace.
Main Results of the Study
This article discusses the concept of digital intellectual property and utilizes social learning theory in order to approach and understand music pirating behavior. It also tests the effects that real life peers and popular media sources as well as online peers and online media sources have on music piracy. More specifically, this article shows that:
- With respect to the offline and online peer measures, the descriptive statistics indicate that students tended to associate with real life peers over online peers, and reported similar levels of agreement concerning the importance of popular media and online media in their influence on MP3 usage. When examining the correlations of the peer variables, real life peers had the strongest association with music piracy, followed by online peers and online media sources.
- Being male and having higher levels of Internet-related skills were also strongly associated with music piracy.
- Students who report being influenced by real life peers seem to be less influenced by other peer sources whether offline or online.
- Students who reported learning the techniques from both online peers and online media sources had significantly higher music piracy scores.
- Students with greater Internet skills and faster connectivity also had significantly higher piracy scores.
Policy Implications as Stated By Author
- The widespread permissiveness of intellectual property theft online, especially among certain populations, needs to be countered through the promotion of information technology ethics and acceptable rules of engagement.
- Those who believe strongly in the inherent wrongfulness of the behavior – and who are substantively affected by it in some shape or form – should work to produce an increasing number of anti-piracy websites, news articles, blogs, and pieces of multimedia content (such as digital images and audio and video podcasts).
Coverage of Study
Datasets
Sample size: | 2032 |
Level of aggregation: | University students |
Period of material under study: | 2003 |