Contest or Contract: Assessing the Effectiveness of Contests in Online Labor Markets

摘要

Online labor markets have become increasingly popular among companies seeking global access to workers. Some platforms have adopted a contest-based scheme, allowing employers to review multiple submissions before selecting the best one. This practice raises a crucial question: Given a predefined budget, should employers choose a contest or contract scheme for tasks? This study examines the comparative effectiveness of contests versus contracts in achieving better job outcomes, focusing on design tasks characterized by high uncertainty. Using a comprehensive data set from a leading online labor market, we employ multiple empirical strategies, combined with machine learning and text mining techniques, to examine the impact of compensation schemes on job quality. Our results suggest that contract-based jobs yield better performance than contests in the context of ideation tasks. Further analysis, such as examining image similarity among pairwise solutions within a task, identifies “copying” behaviors as a key mechanism undermining the effectiveness of contests. In the public setting of contests, workers can observe others’ submissions and revise their own, leading to imitation rather than innovation. As competition intensity increases, workers have weaker incentives to exert effort, reducing solution quality. Consequently, the negative incentive effects in contests outweigh both the potential positive incentive and parallel effects. Moreover, although information availability can enhance work quality, contests inherently offer limited informational advantages compared with contracts. Taken together, our findings suggest that contract-based schemes are more effective than contests for design tasks, offering important implications for both employers and platform designers in online labor markets. This paper was accepted by Karan Girotra, operations management. Funding: N. Zhang acknowledges the support from the National Natural Science Foundation of China [Grant 72302053]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.07082 .