Idea Convergence Quality in Open Innovation Crowdsourcing: A Cognitive Load Perspective

Abstract

Open innovation crowdsourcing enables online crowds to quickly generate a plethora of creative ideas. A key challenge is the convergence of ideas for further consideration from massive numbers of candidate ideas with diverse quality. Based on Cognitive Load Theory, we executed a laboratory experiment to test the associations between three types of cognitive load manipulations and idea convergence outcomes. Our findings show that germane cognitive load positively correlates with idea convergence quality, satisfaction with process, and satisfaction with outcome. Intrinsic cognitive load is negatively associated with satisfaction with process and satisfaction with outcome, while extraneous cognitive load negatively correlates only with satisfaction with outcome. We further identified the positive moderation role of knowledge self-efficacy, perceived goal clarity, and need for cognition on the relationships between germane cognitive load and idea convergence quality. Our findings can inform open innovation organizers when designing tasks and interventions to improve convergence outcomes.