The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach

Open innovation in data science generally takes the form of public competitions where teams exchange messages and solutions by competing and collaborating simultaneously. Team behaviours are widely heterogeneous in terms of the performance of their solutions and the participation in knowledge creation. We present a novel research framework for open innovation by integrating system dynamics and structural topic modelling to extract open factors and adopting a machine learning-based difference-in-differences estimator to understand the impact of team behaviour on their performance using data from Kaggle's competition. Our results identify four team behaviour categories-active, learner, lurker, and passive- in data science open innovation competitions which depend on the performance of their solutions and actions related to posting and reading messages in the forum. Furthermore, the activities of model evaluation, community support, and business understanding are the top three most positive and significant factors affecting team performance. Our research contributes to the literature by highlighting the value of forum feedback and exploring the data science activities in the forum discussion, in relation to innovation performance, to enrich the empirical understanding of open innovation. Research implications for researchers and practitioners participating in, organising, and supporting data science open innovation activities are provided.