Crowdsourcing New Product Ideas under Consumer Learning

Crowdsourcing initiatives are becoming a popular tool for new idea generation for firms. Although such initiatives are widely adopted in many different industries, the number of ideas generated often decline over time, and the implementation rates (percentage of posted ideas that are implemented by the firm) are quite low. Critics of crowdsourcing attribute these observations to three key factors: 1. individuals’ limited view about firms’ products, leading to the contributions of mainly niche ideas; 2. consumers’ limited knowledge about firms’ cost structure, leading to the proposals of mostly infeasible ideas; and 3. firms’ lack of response to customers’ ideas, leading to customer dissatisfaction. To investigate these criticisms in detail and to devise policies for firms to alleviate these concerns, we propose a structural model to capture individual idea contribution dynamics. We estimate the model using a rich dataset obtained from IdeaStorm.com, which is a crowdsourcing website affiliated with Dell. On this website, individuals can contribute ideas and vote on other's ideas. The firm then decides which ideas to implement. Using the peer voting score, we are able to infer the true potential of ideas, whereas the cost to the firm for implementing the idea is indirectly imputed from the idea implementation data. We find that 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 response to contributed ideas. We find that the 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. We also find that an individual’s discontent adversely affects the individual’s continuous participation in idea contributions. As a result of the learning process, the crowdsourcing market becomes more efficient. Contributors of low potential ideas eventually drop out, while the high potential idea contributors remain active. Over time, the average potential of generated ideas increases, while the number of ideas contributed decreases. Hence, the firm can reduce the cost of screening ideas without losing high potential ideas. In our policy simulation, 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 lead to the overall potential of the ideas generated to drop significantly.