A Structural Analysis of the Role of Superstars in Crowdsourcing Contests

2019 | Information Systems Research | Citations: 18

Authors: Zhang, Shunyuan; Singh, Param Vir; Ghose, Anindya

Abstract: We investigate the long-term impact of competing against superstars in crowdsour ... Expand

Abstract: We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve their skills (learn) more when competing against a superstar than otherwise. We show that an individual’s probability of winning in subsequent contests increases significantly more 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 where 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. The results indicate that individuals who greatly prefer monetary reward tend to win fewer 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 significantly push them up the learning curve, leading to higher quality and a higher number of submissions per contest. Overall, our study shows that individuals who are willing to forego short-term monetary rewards by participating in contests with superstars have much to gain in the long term.The online appendix is available at https://doi.org/10.1287/isre.2017.0767. Collapse

Topics: crowdsourcing Topcoder systems design user behavior systems development

Methods: simulation longitudinal research descriptive statistic time series analysis Markov chain Monte Carlo

Theories: information theory

LEARNING-BY-DOING AND PROJECT CHOICE: A DYNAMIC STRUCTURAL MODEL OF CROWDSOURCING

2010 | International Conference on Information Systems | Citations: 0

Authors: Archak, Nikolay; Ghose, Anindya

Abstract: This paper studies determinants of project choice in online crowdsourcing contes ... Expand

Abstract: This paper studies determinants of project choice in online crowdsourcing contests using a unique dataset from the world’s largest competitive software development portal. Particular attention is given to the strategic roles of learning and forward-looking behavior in influencing contestants’ decisions. We use a structural dynamic discrete programming (DDP) model to conduct our analysis and adopt a Bayesian approach to estimation. Our preliminary results provide evidence of learning-by-doing influencing propensities of users to choose projects of different types. The value of the parameter of intertemporal substitution that we identify suggests that while users are forward-looking, the aggregate behavior is far from fully rational. We attribute that result to mix of forward-looking and myopic users in the population. Collapse

Topics: Java crowdsourcing Topcoder systems development software developer

Methods: computational algorithm dynamic programming descriptive statistic simulation robustness check