Examining Multi-Phased Online Crowdsourcing Contests Using Quality Level and Quality Ambiguity

When a platform crowdsources a multi-phased project as multiple contests, it posts a focal phase contest after prior phases' contests are completed. Because a prior phase's winning solution becomes an input in the focal phase contest, this multi-phased setup can be leveraged to objectively gauge the characteristics of the focal contest's problem formulation, which can then help the platform and the solution seeker predict the success of the contest in attracting high quality contestants and generating high quality outcomes. This study develops two measures for the winning solution of a prior phase contest: quality level which is the degree of completeness with which the solution meets user requirements, and quality ambiguity representing the degree of lack of clarity about the quality level. Under the premise that an intrinsic motivation of contestants on crowdsourcing platforms is to solve challenging problems, a theoretical model of the relationships between the two measures and the contest outcome, mediated by the quality of contestants, is developed. Data from a popular software crowdsourcing platform, TopCoder, is used to validate hypotheses on the direct, interaction, and nonlinear impacts of the quality level and quality ambiguity of a prior phase contest's winning solution on the outcome of the focal phase contest. Results show an inverted U-shaped relationship between the quality ambiguity of a prior phase contest's winning solution and the quality of the contestants in the focal phase contest, highlighting a sweet spot for quality ambiguity. Our findings generate important implications for the design of online crowdsourcing contests