Abstract

Organizers of innovation contests frequently rely on non-experts as raters to identify the most promising ideas. These raters often struggle with this cognitively demanding task, which limits their likelihood of selecting high-quality ideas. Building on (digital) nudging theory, this research suggests that partitioning a choice set of ideas into larger subsets (subsets of four vs. two ideas) influences raters’ information processing and cognitive effort, resulting in higher selection quality. We analyze data collected in an eye-tracking experiment involving 59 raters, finding that presenting larger idea subsets (i.e., less partitioning) fosters the selection of more novel ideas. This effect was mediated by raters’ degree of cue-wise information processing and by cognitive effort, which we theorize as the effortful-comparative information processing mechanism. This mechanism is novel to nudging theory as it suggests that nudges not only improve results by facilitating heuristic, less effortful processing of information but also by triggering more effortful-comparative processing of information.

DOI

10.17705/1jais.00958

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