AIS Transactions on Human-Computer Interaction


Innovation contests offer organizations the opportunity to source innovative ideas to achieve competitive advantage. However, raters cannot easily converge on the most promising ideas because they can easily feel overwhelmed by the high number of generated ideas. Further, information overload will likely impair raters’ decision-making processes and how well they can accurately distinguish good from bad ideas. Digital nudging may counteract this convergence challenge via user interface elements to change how information is presented to users. To design a digital nudge in a convergence platform to effectively nudge raters towards improved choice accuracy, one needs to understand the decision-making processes associated with the convergence task. Considering this goal, we conducted an online experiment in which 190 participants eliminated the least promising ideas in presentation modes with either a high (two ideas/screen) or low (30 ideas/screen) decomposition of information load. Our findings suggest that convergence platforms with a high decomposition of information load help raters make more accurate choices. The extent of elimination and revision decisions raters make partially explained this effect. However, these paradoxical mediation effects depended on whether raters showed a high or low tendency to follow the crowd’s opinion. Our findings add to the growing academic knowledge base on idea-selection processes and how one can design convergence platforms with digital nudges to help raters deal with their cognitive constraints and ensure successful convergence.





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