Difference between revisions of "Xia, Huang, Duan and Whinston (2012)"

From Copyright EVIDENCE
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|Abstract=Peer-to-peer sharing networks have seen explosive growth recently. In these networks, sharing files is completely voluntary, and there is no financial reward for users to contribute. However, many users continue to share despite the massive free-riding by others. Using a large-scale data set of individual activities in a peer-to-peer music-sharing network, we seek to understand users’ continued-sharing behavior as a private contribution to a public good. We find that the more benefit users “get from” the network, in the form of downloads, browses, and searches, the more likely they are to continue sharing. Also, the more value users “give to” the network, in the form of downloads by other users and recognition by the network, the more likely they are to continue sharing. Moreover, our findings suggest that, overall, “getting from” is a stronger force for the continued-sharing decision than “giving to.”
 
|Abstract=Peer-to-peer sharing networks have seen explosive growth recently. In these networks, sharing files is completely voluntary, and there is no financial reward for users to contribute. However, many users continue to share despite the massive free-riding by others. Using a large-scale data set of individual activities in a peer-to-peer music-sharing network, we seek to understand users’ continued-sharing behavior as a private contribution to a public good. We find that the more benefit users “get from” the network, in the form of downloads, browses, and searches, the more likely they are to continue sharing. Also, the more value users “give to” the network, in the form of downloads by other users and recognition by the network, the more likely they are to continue sharing. Moreover, our findings suggest that, overall, “getting from” is a stronger force for the continued-sharing decision than “giving to.”
 
|Authentic Link=http://pubsonline.informs.org/doi/pdf/10.1287/isre.1100.0344
 
|Authentic Link=http://pubsonline.informs.org/doi/pdf/10.1287/isre.1100.0344
 +
|Link=http://pubsonline.informs.org/doi/pdf/10.1287/isre.1100.0344
 
|Reference=Adar and Huberman (2000); Golle et al. (2001); Sarouis et al. (2002); Ranganathan et al (2003); Krishnan et al. (2004);
 
|Reference=Adar and Huberman (2000); Golle et al. (2001); Sarouis et al. (2002); Ranganathan et al (2003); Krishnan et al. (2004);
 
|Plain Text Proposition=* A one standard deviation increase in variable of download leads to a 27% increase in the odds of continued sharing. Similarly, a one standard deviation increase of variables of browse, search, contribute, and been_browsed leads to a 23%, 33%, 32%, and 47% increase in the odds of continued sharing, respectively. Becoming a value use leads to a 146% increase in the odds of continued sharing.  
 
|Plain Text Proposition=* A one standard deviation increase in variable of download leads to a 27% increase in the odds of continued sharing. Similarly, a one standard deviation increase of variables of browse, search, contribute, and been_browsed leads to a 23%, 33%, 32%, and 47% increase in the odds of continued sharing, respectively. Becoming a value use leads to a 146% increase in the odds of continued sharing.  
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Because data were extracted through online activity, information on user demographics was nonobservable.
 
Because data were extracted through online activity, information on user demographics was nonobservable.
|Data Year=May 2001 to May 2006
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|Data Year=2001-2006
 
|Data Type=Primary data
 
|Data Type=Primary data
|Data Source=None;
 
 
|Method of Collection=Quantitative Collection Methods, Web analytic (online user trace data), Longitudinal Study
 
|Method of Collection=Quantitative Collection Methods, Web analytic (online user trace data), Longitudinal Study
 
|Method of Analysis=Quantitative Analysis Methods, Descriptive statistics (counting; means reporting; cross-tabulation), Multivariate Statistics, Regression Analysis, Structural Equation Modeling
 
|Method of Analysis=Quantitative Analysis Methods, Descriptive statistics (counting; means reporting; cross-tabulation), Multivariate Statistics, Regression Analysis, Structural Equation Modeling
 
|Industry=Software publishing (including video games); Film and motion pictures; Sound recording and music publishing; Television programmes;
 
|Industry=Software publishing (including video games); Film and motion pictures; Sound recording and music publishing; Television programmes;
 +
|Country=Global;
 
|Cross-country=No
 
|Cross-country=No
 
|Comparative=No
 
|Comparative=No
|Funded By=Not Stated;
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|Government or policy=No
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|Literature review=No
 
}}
 
}}
 
|Dataset={{Dataset
 
|Dataset={{Dataset
 
|Sample Size=300,000,000
 
|Sample Size=300,000,000
 
|Level of Aggregation=Individual data,
 
|Level of Aggregation=Individual data,
|Data Material Year=May 2001 to May 2006
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|Data Material Year=2001-2006
 
}}
 
}}
 
}}
 
}}

Revision as of 11:46, 8 October 2016

Advertising Architectural Publishing of books, periodicals and other publishing Programming and broadcasting Computer programming Computer consultancy Creative, arts and entertainment Cultural education

Film and motion pictures Sound recording and music publishing Photographic activities PR and communication Software publishing (including video games) Specialised design Television programmes Translation and interpretation

1. Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare 2. Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)? 3. Harmony of interest assumption between authors and publishers (creators and producers/investors) 4. Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption) 5. Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)

A. Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right) B. Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction) C. Mass digitisation/orphan works (non-use; extended collective licensing) D. Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability) E. Fair remuneration (levies; copyright contracts) F. Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)

Source Details

Xia, Huang, Duan and Whinston (2012)
Title: To continue sharing or not to continue sharing? An empirical analysis of user decision in peer-to-peer sharing networks
Author(s): Xia, M., Huang, Y., Duan, W., Whinston, A. B.
Year: 2012
Citation: Mu Xia, Yun Huang, Wenjing Duan, Andrew B. Whinston, (2012) Research Note—To Continue Sharing or Not to Continue Sharing? An Empirical Analysis of User Decision in Peer-to-Peer Sharing Networks. Information Systems Research 23(1):247-259.
Link(s): Definitive , Open Access
Key Related Studies:
Discipline:
Linked by:
About the Data
Data Description: Researchers logged all activities and commands sent to one of the largest IRC channels (more than 300 million) called #mp3passion, from March 2001 to May 2006. Researchers defined the time window as two weeks for a certain time period.

During the entire data collection period, the user size of the sharing channel was stable, with around 20,000 unique users (identifiable by user ID), whereas the number of sharers grew from 600 to more than 2,000. The researchers observed 55,031 unique sharers in total, along with 834,613 free riders.

Because data were extracted through online activity, information on user demographics was nonobservable.

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:
  • 2001-2006
Funder(s):

Abstract

Peer-to-peer sharing networks have seen explosive growth recently. In these networks, sharing files is completely voluntary, and there is no financial reward for users to contribute. However, many users continue to share despite the massive free-riding by others. Using a large-scale data set of individual activities in a peer-to-peer music-sharing network, we seek to understand users’ continued-sharing behavior as a private contribution to a public good. We find that the more benefit users “get from” the network, in the form of downloads, browses, and searches, the more likely they are to continue sharing. Also, the more value users “give to” the network, in the form of downloads by other users and recognition by the network, the more likely they are to continue sharing. Moreover, our findings suggest that, overall, “getting from” is a stronger force for the continued-sharing decision than “giving to.”

Main Results of the Study

  • A one standard deviation increase in variable of download leads to a 27% increase in the odds of continued sharing. Similarly, a one standard deviation increase of variables of browse, search, contribute, and been_browsed leads to a 23%, 33%, 32%, and 47% increase in the odds of continued sharing, respectively. Becoming a value use leads to a 146% increase in the odds of continued sharing.
  • Overall, the results show that both self-use and continuous contribution provide strong incentives for users to continue sharing. In addition, “getting-from” is a stronger force for the continued-sharing decision than “giving-to.”
  • Sharing history has a significant effect on users’ decisions to share across all models. The total number of files downloaded has a significantly negative effect on the sharers’ decision to continue sharing.
  • In a PTP network, both the user’s benefits received from the network and the value the user provides to the network are significant predictors of her continued contribution. The level of social interaction and anonymity seems to have little to no effect on whether users continue to share.


Policy Implications as Stated By Author

  • Results may be helpful both to record companies and to copyright holders seeking to design a method to thwart illegal file sharing, and especially to the management of content-sharing communities, such as YouTube and Flickr.
  • Findings indicate the key to network growth is the continuous addition of new and fresh content for users. Further, showing more statistics to make a user’s use of the network more visible will help the user continue sharing. Finally, adding more features to show how a user’s contribution is used by others have a similar positive effect.
  • Accordingly, recognition in visual representation of users’ contributions to the network can be quite effective in motivating a user’s continued contribution.


Coverage of Study

Coverage of Fundamental Issues
Issue Included within Study
Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare
Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)?
Harmony of interest assumption between authors and publishers (creators and producers/investors)
Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption)
Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)
Green-tick.png
Coverage of Evidence Based Policies
Issue Included within Study
Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right)
Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction)
Mass digitisation/orphan works (non-use; extended collective licensing)
Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability)
Green-tick.png
Fair remuneration (levies; copyright contracts)
Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)
Green-tick.png

Datasets

Sample size: 300,000,000
Level of aggregation: Individual data
Period of material under study: 2001-2006