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Abstract Online innovation competitions are ecosystems where institutions source numerous solutions from knowledge workers through a platform intermediary. By considering how an individual competitor’s performance varies based on their social positioning in a competition ecosystem’s collaboration network, we illustrate the value of social networks for individual outcomes in online competitions. The study reports results from Kaggle, a popular online competition platform for data science, where a sample of 350,956 users participated in 2,789 competitions over 4 years. We investigate how the number of collaborations, membership in the largest connected component in the network, and diversity of collaboration experiences impact the points and medals earned and how quickly competitors earn their first medal. Results show that positioning has a positive relationship with performance in competitive ecosystems. Relevant to the future of work, the study considers how knowledge workers in future workplaces should manage their online collaborations. 

Platform designers create and implement incentive systems to encourage users to contribute content to online communities. This article examines the effect of a multidimensional incentive hierarchy in motivating users to engage in competitive and prosocial activities. Utilizing an external change observed in the data science community, Kaggle, and applying a quasi-experimental design, we compared users’ engagement levels before and after introducing a multidimensional incentive hierarchy. We found that implementing a multidimensional incentive system directed users from submitting answers to Kaggle competitions to participating in Kaggle’s online forum discussions. However, our additional analyses suggest that the most and the least motivated users may be less likely to be impacted by such incentives. 

Unlike many STEM fields, data science has emerged with online communities serving as prominent spaces for professional development and learning. This paper explores factors that contribute to gender differences regarding perceptions of satisfaction and difficulty in a learning initiative for data science hosted by the Kaggle community. We investigate multiple factors: prior experience and skills, professional role, and communication within a learning community. Our results, based on a survey of 2,707 aspiring data scientists, suggest that learners who identify as women do not perceive assignments to be more difficult than men, but complete fewer assignments. The increasing difficulty of the learning experience affected all learners, but men were still able to complete the hardest assignments at a higher rate than women despite experiencing similar barriers. Overall, the findings demonstrate how learning initiatives in technically intensive domains contribute to different outcomes between groups.