Social Performances: Understanding the Motivations for Online Participatory Behavior
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Open contribution systems" (OCS) are online applications that encourage users to contribute and share content in a "public" and open manner. While these systems lower the barriers to participating, what is less clear is why users are motivated to contribute time and effort in these online environments with relative strangers. My dissertation proposes that one way to explain high levels of participation on OCS is to use the lens of social performances. This lens suggests that individual participation on social software websites involves elements of both individual and collective performative behavior. The social performance framework suggests that the participatory behavior is part of a larger sensemaking exercise that rationalizes and aligns individual contributions to the collective effort. The view here is that OCS and its users are part of a socio-technical ecology and are mutually dependent on each other. Understanding participation as a form of social performance can enable us to better design systems that encourage participation, collaboration and sharing."
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Number of citations to item: 4
- Sayamindu Dasgupta, William Hale, Andrés Monroy-Hernández, Benjamin Mako Hill (2016): Remixing as a Pathway to Computational Thinking, In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, doi:10.1145/2818048.2819984
- Wanli Xing (2019): Large-scale path modeling of remixing to computational thinking, In: Interactive Learning Environments 3(29), doi:10.1080/10494820.2019.1573199
- Hila Koren, Ido Kaminer, Daphne Ruth Raban (2016): Consume, Modify, Share (CMS): The Interplay between Individual Decisions and Structural Network Properties in the Diffusion of Information, In: PLOS ONE 10(11), doi:10.1371/journal.pone.0164651
- Hila Koren, Ido Kaminer, Daphne R. Raban (2014): Exploring the effect of reinvention on critical mass formation and the diffusion of information in a social network, In: Social Network Analysis and Mining 1(4), doi:10.1007/s13278-014-0185-5