Strategic fit of task-based crowdsourcing: a cybernetic approach

Purpose Relying on digital platforms, crowdsourcing maintains loose connections with crowds, activating them only when tasks arise. While this flexibility offers cost efficiency, it also introduces the challenge of recruitment latency and slow response time. This study aims to examine the conditions under which task-based crowdsourcing is an adaptive choice of organizing. Design/methodology/approach Building on Ashby’s law of requisite variety, we develop an agent-based simulation model to explore how operational performance metrics – efficiency and responsiveness – and overall organizational fit, defined as the optimal level of both, are contingent on internal and environmental complexity drivers. Findings We find that crowdsourcing is the superior choice when both dimensions of environmental complexity – velocity and unpredictability – are very low or very high. In intermediate environments, the choice between crowdsourcing and traditional organizational models depends on the trade-off between efficiency and responsiveness. Traditional models, with their higher worker reliability, excel in responsiveness, particularly in urgent tasks. However, crowdsourcing maintains a better overall fit across varying environmental conditions, making it more suitable for dynamic, turbulent contexts. Additionally, the strength of ties between crowdworkers and the platform can be adjusted as a design parameter to optimize alignment with environmental complexity. Originality/value We contribute by clarifying boundary conditions of the relative advantage of task-based crowdsourcing over traditional forms of organizing. We also contribute to the theory by highlighting the distinction between variety and actual complexity and suggest a mediating role for the latter between the former and organizational performance.