Fuzzy‐set qualitative comparative analysis (fsQCA) for validating causal relationships in system dynamics models

Modelers often create diverse system dynamics models for the same issue, depending on their viewpoints, which can decrease stakeholder assurance. Validating system dynamics may enhance stakeholder confidence. This study suggests using fuzzy‐set qualitative comparative analysis (fsQCA) as a technique based on a set theory approach to validate the causal connections between entities in causal loop diagram (CLD) models. This case study analyzed the issue of Indonesian mobile network operators with limited sample data, utilizing the fsQCA method to test causal connections between entities in the CLD model that require validation. Following the creation of the CLD model through the system dynamics methodology, fsQCA was employed to enhance the previously formed model. The fsQCA method fuses qualitative comparative analysis (QCA) with fuzzy set theory, permitting partial membership, and can identify causal links among entities in the CLD model. It assists in testing causal relationships using limited sample data and boosts stakeholder confidence in the CLD model. Modelers typically produce different system dynamics models for the same problem, depending on each modeler's perspective, leading to reduced stakeholder confidence. Validation of system dynamics can increase stakeholder confidence. The fsQCA method can be used to test causal relationships between entities in the CLD model with small sample data and increase stakeholder confidence in the CLD model.