Crowdsourcing New Product Ideas Under Consumer Learning

Abstract

We propose a dynamic structural model that illuminates the economic mechanisms shaping individual behavior and outcomes on crowdsourced ideation platforms. We estimate the model using a rich data set obtained from IdeaStorm.com, a crowdsourced ideation initiative affiliated with Dell. We find that, on IdeaStorm.com, individuals tend to significantly underestimate the costs to the firm for implementing their ideas but overestimate the potential of their ideas in the initial stages of the crowdsourcing process. Therefore, the “idea market” is initially overcrowded with ideas that are less likely to be implemented. However, individuals learn about both their abilities to come up with high-potential ideas as well as the cost structure of the firm from peer voting on their ideas and the firm’s response to contributed ideas. We find that individuals learn rather quickly about their abilities to come up with high-potential ideas, but the learning regarding the firm’s cost structure is quite slow. Contributors of low-potential ideas eventually become inactive, whereas the high-potential idea contributors remain active. As a result, over time, the average potential of generated ideas increases while the number of ideas contributed decreases. Hence, the decrease in the number of ideas generated represents market efficiency through self-selection rather than its failure. Through counterfactuals, we show that providing more precise cost signals to individuals can accelerate the filtering process. Increasing the total number of ideas to respond to and improving the response speed will lead to more idea contributions. However, failure to distinguish between high- and low-potential ideas and between high- and low-ability idea generators leads to the overall potential of the ideas generated to drop significantly.

The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2443156

Keywords: Crowdsourcing, Structural Modeling, Dynamic Learning, Heterogeneity, Econometric analyses, Utility, Ideation, Bayesian Learning

JEL Classification: M00, M21, M31, D8, O31