Factors influencing the performance of innovation contests
Innovation contests have become a popular mechanism of generating qualified solutions to innovation problems, and they have gained much attention from academia. However, a number of questions for seekers (organizations looking for help from external experts (solvers) to solve innovation problems) remain, for example: how to design contests, how to formulate briefs, how to attract solvers, and how to model the contest process. This dissertation builds a framework to examine the contest process. Using two datasets from a well-known online platform for design tasks, this thesis empirically determines the effect of briefs and the diversity of solvers on the contest performance. Chapter 2 shows that the readability and the length of briefs influence the number of high-skilled and low-skilled solvers, and finally the contest performance. Chapter 3 reveals an in-verted U-shape relationship between the diversity of solvers and the contest perfor-mance, and a moderating effect of the uncertainty of briefs on this nonlinear effect. Chapter 4 demonstrates that the probability of solvers joining the contest and submitting high-quality solutions can be affected by the cumulative number of solvers and the cu-mulative number of high-quality solutions. Thus, this thesis explores three ways by which the contest performance can be affected, and derives new insights of how to im-prove the contest performance by adequately formulating briefs, proactively managing the diversity level among solvers, and properly scoring the candidate solutions.