@inproceedings{3db31ec64cea4f318e9e86438c302f10,
title = "Team dating: A self-organized team formation strategy for collaborative crowdsourcing",
abstract = "Online crowds have the potential to do more complex work in teams, rather than as individuals. However, at such a large scale, team formation can be difficult to coordinate. (How) can we rely on the crowd itself to organize into effective teams? Our research explores a strategy for {"}team dating{"}, a self-organized crowd team formation approach where workers try out and rate different candidate partners. In two online experiments, we find that team dating affects the way that people select partners and how they evaluate them. We use these results to draw useful conclusions for the future of team dating and its implications for collaborative crowdsourcing.",
keywords = "Collaborative crowdsourcing, Self-organization, Team building",
author = "Ioanna Lykourentzou and Kraut, {Robert E.} and Shannon Wang and Dow, {Steven P.}",
note = "Funding Information: The authors acknowledge funding support from the Luxembourg National Research Fund (FNR) grant 734708 and the National Science Foundation (NSF) grants 208382 and 122206. Publisher Copyright: {\textcopyright} 2016 Authors.; 34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 ; Conference date: 07-05-2016 Through 12-05-2016",
year = "2016",
month = may,
day = "7",
doi = "10.1145/2851581.2892421",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery ",
pages = "1243--1249",
booktitle = "CHI EA 2016",
address = "United States",
}