LAUSR

Objectives Collective intelligence (CI), which is defined as shared intelligence emerging from a group of people when they work on the same tasks, is a cornerstone of science where researchers interact and collaborate with each other. However, a new kind of CI through crowdsourcing is emerging by inclusively mobilising people who are not usually involved in research to conduct more innovative research which address the needs of the community. Climate CoLab, an initiative experimenting new ideas to tackle climate change, has mobilised more than 90 000 people to develop and implement more than 2000 research proposals within 7 years since its creation. To determine whether CI through crowdsourcing could change how research is performed, we systematically reviewed methods of mobilising CI through crowdsourcing across different fields of research. Method We searched PubMed, Web of Science, Scopus, EBSCO Business Source Premier, EBSCO Academic Source Premier, Google scholar and resources of the Centre for Collective Intelligence Massachusetts Institute of Technology for all reports describing a research project that had applied methods of CI through crowdsourcing defined as shared intelligence emerging when mobilising people who are usually not involved in the research process to work on a specific task (e.g., solving a problem, generating ideas). We extracted information on the following domains: (1) purposes of using CI, (2) type of participants and methods to recruit participants, (3) motivation, (4) type of participants’ contribution to the project, (5) type of interaction between participants, (6) methods to evaluate participants’ contribution and decision making process, (7) challenges and drawbacks of CI reported by authors and authors’ satisfaction with participants’ contributions. We applied content analysis to inductively develop themes and categories for each domain. Results We identified 141 reports. CI was mobilised to generate ideas, to evaluate ideas/work, to solve problem (e.g., diagnose disease, analyse and interpret data), or to create intellectual products. Most research projects (76%) were open to the public without restriction to the expertise of participants. Incentives to participants and intrinsic motivation were reported in 74% of articles. Of those reported, 38% were financial incentives, followed by recognition from the network (8%), access to data (2%). Independent contribution (i.e., no interaction with other participants) (37%), collaboration (31%), competitions (26%), and playing games (11%) were the methods by which participants contributed to projects. 61% of articles reported methods of evaluation of participants’ contribution and decision-making process with 43% of projects using an independent panel of experts and 18% involving end-users in evaluation and decision making. Some challenges in implementation and sustainability of collective intelligence were reported. Conclusions Our results provide an in-depth description of methods for mobilising collective intelligence and we propose a framework to facilitate its use in research. However, more research is needed to understand the conditions which enable and constrain the success of collective intelligence.