A les 12 hores.

El títol complet és: Can AI Help in Crowdsourcing? Testing Alternate Algorithms for Idea Screening in Crowdsourcing Contests

En línia.

A càrrec de Christian Pescher (Marketing).

Crowdsourcing, while a boon to ideation, generates thousands of ideas. Screening these ideas to select a few winners is a major challenge because of the limited number, expertise, objectivity, and attention of judges. Artificial intelligence (AI) may help. This paper compares three original and extended versions of AI algorithms from marketing to evaluate ideas: Word Colocation, Content Atypicality, and Inspiration Redundancy. Each algorithm suggests predictors of winning ideas. The authors extend these predictors and apply two methods for finding parsimonious predictors: least average shrinkage and selection operator (LASSO) and K-sparse Exhaustive Search, for K<=5. The authors test the algorithms on 20 crowdsourcing contests conducted for large firms. The standard provided by the client is "drop the worst 25% of ideas without sacrificing more than 15% of good ideas," as ranked by experts. Results are the following. First, of the three original algorithms, Inspiration Redundancy performs best out-of-sample, but fails to meet the 15% threshold. Second, for two of the three algorithms, the extended versions outperform the original. In particular, Topic Overlap Atypicality, a new measure, emerges as the most robust predictor. Third, when the best versions of the algorithms are used, all three contribute to the important out-of-sample prediction accuracy.