Search

Brands and advertisers often tap into the crowd to generate ideas for new products and ad creatives by hosting ideation contests. Content evaluators then winnow thousands of submitted ideas before a separate stakeholder, such as a manager or client, decides on a small subset to pursue. We demonstrate the information value of data generated by content evaluators in past contests and propose a proof-of-concept machine learning approach to efficiently surface the best submissions in new contests with less human effort. The approach combines ratings by different evaluators based on their correlation with the past stakeholder choices, controlling for submission characteristics and textual content features. Using field data from a crowdsourcing platform, we demonstrate that the approach improves performance by identifying nonlinear transformations and efficiently reweighting evaluator ratings. Implementing the proposed approach can affect the optimal assignment of internal experts to ideation contests. Two evaluators whose votes were a priori equally correlated with sponsor choices may provide substantially different incremental information to improve the model-based idea ranking. We provide additional support for our findings using simulations based on a product design survey.

Online crowdfunding is a popular new tool for raising capital to commercialize product innovation. Product innovation must be both novel and useful (1-4). Therefore, we study the role of novelty and usefulness claims on Kickstarter. Startlingly, we find that a single claim of novelty increases project funding by about 200%, a single claim of usefulness increases project funding by about 1200%, and the co-occurrence of novelty and usefulness claims lowers funding by about 26%. Our findings are encouraging because they suggest the crowd strongly supports novelty and usefulness. However, our findings are disappointing because the premise of crowdfunding is to support projects that are innovative, i.e. that are both novel and useful, rather than projects that are only novel or only useful.

We study whether R&D-intensive firms are more resilient to trade shocks. We correct for the endogeneity of R&D using tax-induced changes to R&D cost. While rising imports from China lead to slower sales growth and lower profitability, these effects are significantly smaller for firms with a larger stock of R&D (by about half when moving from the bottom quartile to the top quartile of R&D). As a result, while firms in import-competing industries cut capital expenditures and employment, R&D-intensive firms downsize considerably less. Finally, we provide evidence that these effects are explained by R&D allowing firms to increase product differentiation.