Successful Trajectories: How Winning Strategies Differ for Individuals in Contests on a Crowdsourcing Platform

Abstract

Crowdsourcing platforms provide opportunities to showcase creativity, collaborate, and compete for recognition and success. However, not all projects gain equal attention—strategic actions taken by ideators play a crucial role in determining which projects attract visibility and community support.

This study investigates how the timing and evolution of strategic actions influence project success on a global crowdsourcing platform. Using a multi- method research design, the project integrates machine learning techniques with real-world platform data to uncover patterns linked to project outcomes. At this stage, the analysis focuses on the first step: classification modelling that predicts project success based on early strategic actions. Preliminary findings suggest that sustained engagement is among the strongest predictors of project success. Subsequent steps will explore the temporal dynamics of engagement using survival analysis and identify trajectories through clustering. The study aims to show that success strategies form a reinforcing continuum rather than functioning as isolated decisions. These insights will contribute to theories of strategic behavior in crowdsourcing platforms and offer practical implications for platform participants and managers.

Co-authors: Michela Beretta and Lars Frederiksen