Experienced system dynamicists are well aware of the benefits of conceptualising complex cause and effect relationships and feedback loops using Causal Loop Diagrams (CLDs). To ensure model completeness, it is important to justify the method as fit for the purposes of the study. Although a small number of methods exist to construct CLDs from qualitative data obtained from participants with little experience in system dynamics, little processual guidance exists to inform method selection. Guidance in conducting these studies is of particular importance when researchers aim to draw inference by comparing theory-driven and practitioner data-driven CLDs. As a proof of concept, we present a case study including a theoretically derived CLD and a CLD developed in various group model building sessions aimed at understanding causal relationships and necessary factors for supply chain cluster formation. Variety in CLD construction methods allowed for the exploration of methodological concepts such as iteration and convergence of CLDs as well as aggregation of multiple CLDs. A critical commentary is presented on the various methods aimed at informing method selection.