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@article{whiting2020parallel,

title = {Parallel Worlds: Repeated Initializations of the Same Team To Improve Team Viability},

author = {Mark Whiting and Irena Gao and Michelle Xing and Junior Diarrassouba N’godjigui and Tonya Nguyen and Michael Bernstein},

url = {https://www.researchgate.net/profile/Mark_Whiting3/publication/341743251_Parallel_Worlds_Repeated_Initializations_of_the_Same_Team_to_Improve_Team_Viability/links/5ed6931045851529452a2738/Parallel-Worlds-Repeated-Initializations-of-the-Same-Team-to-Improve-Team-Viability.pdf},

doi = {10.1145/3392877},

year  = {2020},

date = {2020-05-01},

journal = {Proceedings of the ACM Human-Computer Interaction},

volume = {4},

number = {CSCW1},

pages = {22},

abstract = {A team’s early interactions are influential: small behaviors cascade, driving the team either toward successful collaboration or toward fracture. Would a team be more viable if it could undo initial interactional missteps and try again? We introduce a technique that supports online and remote teams in creating multiple parallel worlds: the same team meets many times, led to believe that each convening is with a new team due to pseudonym masking while actual membership remains static. Afterward, the team moves forward with the parallel world with the highest viability by using the same pseudonyms and conversation history from that instance. In two experiments, we find that this technique improves team viability: teams that are reconvened from the highest-viability parallel world are significantly more viable than the same group meeting in a new parallel world. Our work suggests parallel worlds can help teams start off on the right foot — and stay there.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{whiting2019did,

title = {Did It Have To End This Way? Understanding the Consistency of Team Fracture},

author = {Mark Whiting and Allie Blaising and Chloe Barreau and Laura Fiuza and Nik Marda and Melissa Valentine and Michael Bernstein},

url = {https://dl.acm.org/doi/pdf/10.1145/3359311},

doi = {10.1145/3359311},

year  = {2019},

date = {2019-11-01},

journal = {Proceedings of the ACM Human-Computer Interaction},

volume = {3},

number = {209},

pages = {23},

abstract = {Was a problematic team always doomed to frustration, or could it have ended another way? In this paper, we study the consistency of team fracture: a loss of team viability so severe that the team no longer wants to work together. Understanding whether team fracture is driven by the membership of the team, or by how their collaboration unfolded, motivates the design of interventions that either identify compatible teammates or ensure effective early interactions. We introduce an online experiment that reconvenes the same team without members realizing that they have worked together before, enabling us to temporarily erase previous team dynamics. Participants in our study completed a series of tasks across multiple teams, including one reconvened team, and privately blacklisted any teams that they would not want to work with again. We identify fractured teams as those blacklisted by half the members. We find that reconvened teams are strikingly polarized by task in the consistency of their fracture outcomes. On a creative task, teams might as well have been a completely different set of people: the same teams changed their fracture outcomes at a random chance rate. On a cognitive conflict and on an intellective task, the team instead replayed the same dynamics without realizing it, rarely changing their fracture outcomes. These results indicate that, for some tasks, team fracture can be strongly influenced by interactions in the first moments of a team’s collaboration, and that interventions targeting these initial moments may be critical to scaffolding long-lasting teams.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{whiting2019fair,

title = {Fair work: Crowd work minimum wage with one line of code},

author = {Mark Whiting and Grant Hugh and Michael Bernstein},

url = {https://ojs.aaai.org/index.php/HCOMP/article/download/5283/5135/},

year  = {2019},

date = {2019-10-28},

journal = {Proceedings of the AAAI Conference on Human Computation and Crowdsourcing},

volume = {7},

number = {1},

pages = {197-206},

abstract = {Accurate task pricing in microtask marketplaces requires substantial effort via trial and error, contributing to a pattern of worker underpayment. In response, we introduce Fair Work, enabling requesters to automatically pay their workers minimum wage by adding a one-line script tag to their task HTML on Amazon Mechanical Turk. Fair Work automatically surveys workers to find out how long the task takes, then aggregates those self-reports and auto-bonuses workers up to a minimum wage if needed. Evaluations demonstrate that the system estimates payments more accurately than requesters and that worker time surveys are close to behaviorally observed time measurements. With this work, we aim to lower the threshold for pro-social work practices in microtask marketplaces.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}