Abstract
Self-selection on the basis of problem-solving capabilities constitutes a powerful and convincing principle in crowdsourcing. The self-selection principle describes that among the huge mass of potential problem solvers particularly those individuals decide to participate in the contest who have the best problem-solving capabilities with regard to the problem at question. The classic argument from tournament theory is that higher skills increase the individual’s chance of winning. However, there are also counter arguments: High-skilled potential participants may also have better alternative options to invest in than low-skilled individuals. We investigated how effectively self-selection in crowdsourcing works and how seekers can boost self-selection? Extant research on crowdsourcing success factors does not provide answers to this as studies exclusively rely on participants, i.e. the “survivors” of the self-selection process. By applying a unique research design - combining behavioral data from a real crowdsourcing contest with data from a survey and archival data - we were able to overcome several methodological challenges. Our findings suggest that individuals with strong skills indeed tend to self-select into the contest and those with lower skills tend to self-select out. However, this desired self-selection effect is far from being perfect as the effect size is only moderate. We also observe a significant difference in idea quality between high-skilled and low-skilled individuals. To mitigate the loss of high-skilled individuals and subsequently increase the quality of the contest output, our analyses emphasize the importance of the award structure and distribution in the self-selection process.