<p>Traditional analytical methods often struggle to capture the inherent complexity of real-world phenomena, particularly in the business and management literature. To address this, researchers are increasingly adopting complexity theory and methodologies such as fuzzy set Qualitative Comparative Analysis (fsQCA), yet some issues still surround the decision of sufficient conditions in fsQCA, causing unclear interpretations and reducing analytical clarity. To overcome these issues, this research proposes a systematic and rigorous procedure for effectively identifying the cases from a dataset and generating corresponding solutions that enhances case selection and sufficiency testing by incorporating structured steps. Each solution is generated by cases. Importantly, the cases retained through the proposed method fall within the region of typical cases in the XY-plot, thereby avoiding the issue of ambivalent subset relationships observed in fsQCA applications. This improves clarity in interpreting causal sufficiency. The proposed approach opens new avenues for addressing more complex analytical challenges in fsQCA, setting up multi-layered antecedent structures, and giving a foundation for integrating fuzzy inference systems with fsQCA.</p>