Facilitating like Darwin: Supporting cross-fertilisation in crowdsourcing

Abstract

Humankind faces many “wicked” decision-making problems, which must be solved. One promising approach refers to crowdsourcing systems that hold the potential to solve any kind of problem – notably wicked ones. Crowdsourced solutions work well because crowds exchange knowledge from different domains – a concept known as “cross-fertilisation.” Thereby, the “facilitator” of a crowdsourcing system is the primary decision maker when it comes to specifying and managing the crowd. The facilitator's role includes actively managing cross-fertilisation. However, in the light of technological advancements and large-scale data, facilitation proves difficult – especially in one particular type of crowdsourcing – crowdsolving. Thus, academia recently called for relieving some burden of facilitators and started developing tools for supporting or automated facilitation. Yet, the focus of existing tools is not on fostering the innermost core of crowdsolving endeavours – cross-fertilisation. By taking a design science perspective, we propose design principles and design guidelines for a decision-support tool aiding facilitators to (a) set the boundary conditions for, (b) measure, and (c) facilitate cross-fertilisation. We evaluate feasibility and value added of the abstract design by applying it to different crowdsolving platforms including a prototypical implementation and qualitative evaluation by facilitators.