On Crowdsourcing Mode and Its Timing Strategy— —Based on big data analysis

摘要

Crowdsourcing is a new model that helps companies solve problems with the help of external network intellectual resources. How to attract more solvers to participate is not only a problem in the practical application of crowdsourcing, but also the key to crowdsourcing research. Based on the social learning theory, this paper empirically studies the effect of scheduling on the numbers of submitted solution, as well as the moderating role of bonus and task category. This article applied 386 thousands real crowdsourcing projects’ secondary data to test the hypothesis. The results show that: scheduling has a positive effect on the numbers of submitted solutions. Based on task category, a suitable scheduling can effectively more solvers. And for time-critical tasks, corporates can set a higher prize quantity to attract more participants.