Imam, Sana HassanMetz, Christopher AlexanderHornuf, LarsDrechsler, Rolf2023-08-242023-08-242023https://dl.gi.de/handle/20.500.12116/42142Crowdsourcing platforms connect companies with heterogeneous users to create innovation ecosystems. However, platforms often have difficulty keeping users active. User engagement – the participation, interaction, and commitment among online users engaging in collaborative activities – is crucial to the continued success of these platforms. This paper presents a new approach to predicting whether a user will engage with online idea crowdsourcing platforms as a short-term or long-term user, applying a machine learning model that boasts 96% accuracy. By utilizing Explainable Artificial Intelligence (XAI)-SHapley Additive exPlanations (SHAP), we propose a framework for future research into user engagement patterns and trends across different contexts. This framework can assist platform administrators in recognizing and rewarding valuable users, ultimately leading to the lasting success of online idea crowdsourcing platforms.Text/Workshop Paper10.18420/muc2023-mci-ws16-385