Applying Fair Reward Divisions to Collaborative Work
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Collaborative crowdsourcing tasks allow workers to solve more difficult problems than they could alone, but motivating workers in these tasks is complex. In this thesis, we study how to use payments to motivate groups of crowd workers. We leverage concepts from equity theory and cooperative game theory to understand the connection between fair payments and motivation. Based on findings from a systematic literature review, we identify how the implications of equity theory relate to the Mechanical Turk ecosystem. Then, we use a realistic audio transcription task to evaluate how theoretically fair payments affect crowd workers. Further, we carry out two experiments to find how people’s perceptions of fair rewards differ from cooperative game theory’s fairness axioms. Our findings have important implications for designing collaborative work and directing future research on perceptions of fairness.
Cite this version of the work
Gregory d'Eon (2019). Applying Fair Reward Divisions to Collaborative Work. UWSpace. http://hdl.handle.net/10012/14811