As a type of open innovation, emerging crowdsourcing platforms have garnered significant attention from users and companies. This study aims to determine how online seeker signals affect the user participation behavior of the solver in the open innovation crowdsourcing community, by means of which to achieve the long-term sustainable development of the emerging crowdsourcing platform. We performed data analysis based on the system of regression equation approach in order to conduct quantitative research. We found that online reputation and salary comparison positively influence user participation behavior, and that interpersonal trust acts as a strong mediator in the relationship between salary comparison and user participation behavior. In addition, we observed an elevation in task information diversification as a moderator, which positively affects online seeker signals on user participation behavior. Furthermore, an upsurge was noted in task information overload as a moderator, which adversely affects online seeker signals on user participation behavior. The contributions of this article include the application of the innovative signal transmission model, and online task information quality has important guiding significance on how to design task descriptions for emerging crowdsourcing platforms in order to stimulate user participation behavior.