Debiasing Crowdsourcing and Collective Intelligence for Open Innovation with Novel Information Systems Affordances

Effective and sustainable open innovation is essential for firm success. Open Innovation (OI) is often enabled by information systems (IS) such as crowdsourcing and collective intelligence platforms because they bring benefits not possible using traditional methods for idea generation. However, decision making during the idea screening, evaluation and selection stage of the innovation process tend to be affected by a range of biases that existing IS have not successfully overcome. This paper describes a design science research project that created a new IS artefact with novel affordances based on debiasing strategies and new aggregation methods based in Bayesian epistemology. The IS was designed to support user engagement with evidence-based innovation hypotheses and debias decisions during the innovation process. Preliminary evaluation of the artefact, contributions to IS literature and future research opportunities are discussed.