A Structural Analysis of the Role of Superstars in Crowdsourcing Contests

Abstract

In crowdsourcing contests, participants solve a well-defined problem and compete for a fixed prize. Past studies as well as anecdotal evidence have shown that individuals may avoid competing with superstar participants (the ones whose skills are far more superior to others) because their chances of winning a contest reduce significantly when competing against a superstar. This adverse “superstar” effect can lead to thinly competed contests, which has raised concerns for project sponsors as well as practitioners as prior research has shown that the chances of achieving an extreme high quality solution increases with number of participants. In this study, we argue that the superstar effect highlighted earlier only considers superstar as a competitor and ignores her as a potential source to learn from. We illustrate a learning effect where participants are able to improve their skills (learn) more when competing against a superstar by interacting with and observing the best in action. We use a unique panel dataset from Topcoder.com to document this learning effect. We show that an individual’s probability of winning in subsequent contests increases significantly after she has participated in a contest with a superstar coder than otherwise. We build a dynamic structural model with individual heterogeneity where individuals choose contests to participate in and learning in a contest happens through an information theory based Bayesian learning framework. We find that individuals with lower ability to learn tend to value monetary reward highly, and vice versa. Results indicate that individuals who prefer monetary reward highly tend to win less contests as they rarely achieve the high skills needed to win a contest. Counterfactual analysis suggests that instead of avoiding superstars, individuals should be encouraged to participate in contests with superstars early on as it can significant push them up the learning curve leading to higher quality and number of submissions per contest. Further, incentivizing individuals who value monetary reward highly provides better returns than incentivizing everyone. Another counterfactual analysis shows that Topcoder would benefit from helping improve the learning of individuals with high preference for monetary reward and low learning ability. This is because when individuals achieve high skills, it is the ones who value monetary reward highly who become significantly more active in participating in contests. Overall, our study shows individuals who are willing to forego short-term monetary rewards by participating in contests with superstars have a lot to gain in the long term.