Determinants of Crowdsourcing Openness in High-Urgency Conditions: A Configurational Approach

Managers increasingly use crowdsourcing to address problems in high-urgency conditions. However, to maximize its benefits, they must determine the appropriate level of openness of crowdsourcing challenges—openness in knowledge sourcing and openness in knowledge use. In this study, we examine how configurations of factors (e.g., problem attributes, crowdsourcing design elements, and solution seeker characteristics) influence crowdsourcing openness in high-urgency conditions. Drawing on a dataset of 162 crowdsourcing challenges launched between 2020 and 2022 to address Covid-19-related problems, we apply fuzzy-set qualitative comparative analysis (fsQCA) to uncover the configurations of factors that lead to different levels of openness in knowledge sourcing and knowledge use. Our findings indicate that openness—particularly openness in knowledge use—can be achieved through various configurations by different types of seekers. These insights provide practical guidance for balancing crowdsourcing openness under urgent conditions to maximize innovation and efficiency.