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Area of Science:

  • * Computer Science
  • * Social Sciences
  • * Human-Computer Interaction

Background:

  • * Modern crowdsourcing enables complex task solutions through collective labor.
  • * Manual team formation is challenging at scale, leading to algorithmic solutions.
  • * Algorithmic, top-down team formation often results in poor collaboration and worker dissatisfaction.

Purpose of the Study:

  • * To investigate and compare three crowd team formation models: bottom-up, top-down, and hybrid.
  • * To evaluate team competitiveness and teamwork quality in simulated open collaboration scenarios.
  • * To identify conditions under which different team formation strategies are most effective.

Main Methods:

  • * Simulation of an open collaboration scenario, such as a hackathon.
  • * Evaluation of team formation models based on worker profiling and task objectives.
  • * Analysis of team competitiveness and teamwork quality across different models.

Main Results:

  • * The bottom-up team formation model yielded the most competitive teams with superior teamwork quality.
  • * Bottom-up approaches are particularly effective for workers with high-risk appetites and high homophily.
  • * Algorithmic, top-down methods can lead to alienation, clashes, and dissatisfaction.

Conclusions:

  • * Integrating worker agency into algorithm-mediated team formation is crucial for collaborative/competitive settings.
  • * Bottom-up team formation enhances worker satisfaction and collaboration effectiveness.
  • * Findings have practical implications for designing large-scale crowdsourcing platforms.