A method for Smart Idea Allocation in crowd-based idea selection

When evaluating ideas, raters can quickly experience cognitive overload that might result in poor selection performance. Contest managers have an interest in designing idea evaluation tasks that reduce the expected cognitive load of raters. However, research on how managers can meaningfully allocate ideas to raters to manage cognitive load is limited. Moreover, it is unclear how decision support (systems) should be designed in order to help managers to effectively allocate ideas. This paper addresses this challenge and suggests nine design principles and an approach for Smart Idea Allocation (SIA) as a design artifact, which chunks ideas into small subsets, utilizes cognitive biases, and fairly distributes expected cognitive load among raters. We evaluated the SIA approach on a sample of 525 ideas and compared its performance with a random allocation. Our findings suggest that SIA can utilize potential cognitive biases (salience, herding, and order and anchoring bias) more successfully than a random allocation would, distributes the expected cognitive load more fairly among raters, and requires fewer raters for the evaluation task than a random allocation approach. These findings have implications for research on idea selection and are useful for managers of innovation contests. [Display omitted] •We propose an approach for automated allocation of ideas in innovation contests•Nine design principles aim at utilizing cognitive biases and reducing cognitive load•This paper offers managers decision support to optimally allocate ideas to raters•The effectiveness of the allocation approach is evaluated with performance criteria