A Theoretical and Empirical Investigation of Feedback in Ideation Contests

Ideation contests are commonly used across public and private sectors to generate new ideas for solving problems, creating designs, and improving products or processes. In such a contest, a firm or an organization (the seeker) outsources an ideation task online to a distributed population of independent agents (solvers) in the form of an open call. Solvers compete to exert efforts and the one with the best solution wins a bounty. In evaluating solutions, the seeker typically has subjective taste that is unobservable to solvers. In practice, the seeker often provides solvers with feedback, which discloses useful information about her private taste. In this study, we develop a game-theoretic model of feedback in unblind ideation contests, where solvers' solutions and the seeker's feedback are publicly visible by all. We show that feedback plays an informative role in mitigating the information asymmetry between the seeker and solvers, thereby inducing solvers to exert more efforts in the contest. We also show that some key contest and solver characteristics (CSC, including contest reward, contest duration, solver expertise, and solver population) have a direct effect on solver effort. Interestingly, by endogenizing the seeker's feedback decision, we find that the optimal feedback volume increases with contest reward, contest duration, solver expertise, but decreases with solver population. Thus, CSC elements also have an indirect effect on solvers' effort level, with feedback volume mediating this effect. Employing a dataset from , a leading online ideation platform in China, we find empirical evidence that is consistent with these theoretical predictions.