Although crowd-ideation has harnessed digital technologies to tap on the wisdom of the crowd in solving a wide range of business problems, it suffers from issues of redundancy and wasted effort when compared to its traditional offline counterpart. Consequently, an elusive challenge for crowd-ideation platforms stems from how asynchronous collaboration can be fostered among contributors to achieve cumulative knowledge integration. Building on Cognitive Load Theory, this study delineates among three types of cognitive load in collaborative tasks and advances three analytics-driven design features that can be incorporated into crowd-ideation platforms to bolster contributors’ efficiency in knowledge integration. A laboratory experiment is further proposed to validate our hypotheses. Findings from this study can contribute towards a deeper understanding of how crowd-ideation platforms can be designed to promote cumulative knowledge integration among contributors by decreasing information overload and configure cognitive load distribution.
Fu, Mengyao and Wang, Weiquan, "Facilitating Collaboration on Crowd-Ideation Platforms through Cognitive Load Configuration" (2021). PACIS 2021 Proceedings. 261.
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