Qualitative meta‐analysis with system dynamics modeling for process theory development

Management research has been dominated by variance theories that explain outcomes through statistical associations. While process theory complements variance theory by focusing on how and why events, activities, and choices interact over time, it remains challenging to acquire rich, longitudinal process data and translate them into novel, abstract process theories. We propose integrating qualitative meta-analysis (QMA) with system dynamics (SD) modeling to address these challenges. QMA systematically synthesizes temporally rich evidence across multiple qualitative case studies, while SD provides a formal language for articulating causal mechanisms, accommodating temporal complexity, and testing dynamic hypotheses. Using a recent application to servitization, we illustrate how QMA-SD can be implemented in a step-by-step manner. Beyond its substantive contribution to the servitization literature, we demonstrate the broader potential of QMA-SD for generating empirically grounded, generalizable, and testable process theories.