Coordination and Efficiency in Decentralized Collaboration

Environments for decentralized on-line collaboration are now widespread on the Web, underpinning open-source efforts, knowledge creation sites including Wikipedia, and other experiments in joint production. When a distributed group works together in such a setting, the mechanisms they use for coordination can play an important role in the effectiveness of the group's performance. Here we consider the trade-offs inherent in coordination in these on-line settings, balancing the benefits to collaboration with the cost in effort that could be spent in other ways. We consider two diverse domains that each contain a wide range of collaborations taking place simultaneously -- Wikipedia and GitHub -- allowing us to study how coordination varies across different projects. We analyze trade-offs in coordination along two main dimensions, finding similar effects in both our domains of study: first we show that, in aggregate, high-status projects on these sites manage the coordination trade-off at a different level than typical projects; and second, we show that projects use a different balance of coordination when they are "crowded," with relatively small size but many participants. We also develop a stylized theoretical model for the cost-benefit trade-off inherent in coordination and show that it qualitatively matches the trade-offs we observe between crowdedness and coordination.Comment: 10 pages, 6 figures, ICWSM 2015, in Proc. 9th International AAAI Conference on Weblogs and Social Medi

Herding a Deluge of Good Samaritans: How GitHub Projects Respond to Increased Attention

Collaborative crowdsourcing is a well-established model of work, especially in the case of open source software development. The structure and operation of these virtual and loosely-knit teams differ from traditional organizations. As such, little is known about how their behavior may change in response to an increase in external attention. To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching. In carrying out our research, we use the lens of organizational change, which considers the challenges teams face during rapid growth and how they adapt their work routines, organizational structure, and management style. We show that trending results in an explosive growth in the effective team size. However, most newcomers make only shallow and transient contributions. In response, the original team transitions towards administrative roles, responding to requests and reviewing work done by newcomers. Projects evolve towards a more distributed coordination model with newcomers becoming more central, albeit in limited ways. Additionally, teams become more modular with subgroups specializing in different aspects of the project. We discuss broader implications for collaborative crowdsourcing teams that face attention shocks.National Science Foundation Grant No. IIS-1617820.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153786/1/Maldeniya et al. 2020.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153786/4/Maldeniya et al. 2020 Published Version.pdfDescription of Maldeniya et al. 2020.pdf : Main ArticleDescription of Maldeniya et al. 2020 Published Version.pdf : Published Versio

The Influence of Diversity and Experience on the Effects of Crowd Size

One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about the relationship between the size of crowds and its impact on outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance based on 4,317 articles in the WikiProject Film community. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136213/1/Robert and Romero 2017 (JAIST).pd