Abstract

Open idea evaluation is based on the vision of leveraging the crowd’s wisdom for screening and evaluation of early stage innovations. However, when being confronted with the task to evaluate a large number of idea proposals, cognitive capacity of participants in open idea evaluation is challenged. Given the diminishing effect of cognitive load on the ability to make elaborate deci-sions, this paper aims to answer the question of how to lower cognitive load of participants in open idea evaluation. Therefore, we leverage knowledge from research in choice architecture, a concept incorporating tools to influence decisions by the design of decision situations. We derive two design variations–partitioning of options and decision staging–and propose an experimental design for their evaluation in a laboratory experiment. With the proposed study, we aim to con-tribute to theory by combining knowledge from choice architecture with the design of crowdsourcing platforms. Consequently, we aim to provide novel insights into decision support for crowdsourced decision tasks.

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