Identifying New Product Ideas: Waiting for the Wisdom of the Crowd or Screening Ideas in Real Time

Crowdsourcing ideas from consumers can enrich idea input in new product development. After a decade of initiatives (e.g., Starbucks’ MyStarbucksIdea, Dell's IdeaStorm), the implications of crowdsourcing for idea generation are well understood, but challenges remain in dealing with the large volume of rapidly generated ideas produced in crowdsourcing communities. This study proposes a model that can assist managers in efficiently processing crowdsourced ideas by identifying the aspects of ideas that are most predictive of future implementation and identifies three sources of information available for an idea: its content, the contributor proposing it, and the crowd's feedback on the idea (the “3Cs”). These information sources differ in their time of availability (content/contributor information is available immediately; crowd feedback accumulates over time) and in the extent to which they comprise structured or unstructured data. This study draws from prior research to operationalize variables corresponding to the 3Cs and develops a new measure to quantify an idea's distinctiveness. Applying automated information retrieval methods (latent semantic indexing) and testing several linear methods (linear discriminant analysis, regularized logistic regression) and nonlinear machine-learning algorithms (stochastic adaptive boosting, random forests), this article identifies the variables that are most useful towards predicting idea implementation in a crowdsourcing community for an IT product (Mendeley). Our results indicate that consideration of content and contributor information improves ranking performance between 22.6 and 26.0% over random idea selection, and that adding crowd-related information further improves performance by up to 48.1%. Crowd feedback is the best predictor of idea implementation, followed by idea content and distinctiveness, and the contributor's past idea-generation experience. Firms are advised to implement two idea selection support systems: one to rank new ideas in real time based on content and contributor experience, and another that integrates the crowd's idea evaluation after it has had sufficient time to provide feedback.