Many approaches for process variant management employ a reference model for deriving a target variant either using configuration or adaptation mechanisms. What is missing at this stage is empirical insight into their relative strengths and weaknesses. Our paper addresses this gap. We selected C-YAWL and vBPMN for a comparative, empirical user study. Both approaches center on a reference process, but provide different types of configuration and adaptation mechanisms as well as modularization support. Along with this aspect, we investigate the effect of model complexity and professional level on human process variant modeling performance. Given unlimited processing time, we could not show that complexity or the participant's professional level significantly impacts the task success rate or user contentment. Yet, an effect of model complexity can be noted on the execution speed for typical variant maintenance tasks like the insertion and deletion of process steps. For each of the performance measures of success rate, user contentment and execution speed, vBPMN performs significantly better than C-YAWL. We argue that this is due to vBPMN's advanced modularization support in terms of pattern-based process adaptations to construct process variants. These insights are valuable for advancing existing modeling approaches and selecting between them. © 2013 Elsevier Ltd.