## 5 Results and Robustness Analysis  
### Results  
As described above, the first step in fsQCA is calibration, which involves transforming variables into fuzzy sets. This process assigns a degree of membership to each case, indicating the extent to which it belongs to a particular set. These sets are referred to as \"conditions\", analogous to independent variables, and \"outcome\", analogous to the dependent variable. Therefore, the raw data were transformed into fuzzy sets with membership values ranging from 0 to 1, representing the degree to which each case belongs to a set. In this study, we implemented a clustering approach to establish the thresholds, specifically employing the Euclidean distance method. In [55], the author notes that cluster analysis is instrumental in identifying the optimal cutoff points, as it efficiently partitions the scores into distinct groups, thereby segmenting the original data into the most meaningful clusters. This process enables us to identify the three thresholds required for calibration: full membership (1.0), full non-membership (0.0), and a crossover point (0.5), where a case is equally in and out of the set. FsQCA produces three types of solutions: complex, intermediate, and parsimonious. Each solution represents a different level of simplification, with the complex solution being the most comprehensive and the parsimonious solution being the most simplified [45]. All solutions adhere to Boolean logic and are free of contradictions. The complex solution, which includes all possible configurations of conditions sufficient for the outcome, offers a detailed but potentially unmanageable picture of causal relationships. Its complexity can make interpretation challenging, particularly when numerous conditions are involved. In contrast to the complex solution, the parsimonious solution simplifies causal configurations by retaining only the prime implicants, which are the minimal combinations of conditions indispensable for the outcome to occur. However, this simplification involves making assumptions about logical remainders, potentially leading to conclusions not fully supported by empirical evidence or theoretical understanding. In [45], the author does not recommend relying on these simplification mechanisms and proposes focusing on the complex or intermediate solutions, which are more closely grounded in the empirical data. The intermediate solution offers a balance between complexity and parsimony by incorporating theoretical expectations to simplify the causal analysis. These expectations, supported by existing research and knowledge, guide the selection of specific causal configurations. Effectively utilizing the intermediate solution requires a strong theoretical understanding of the relationships between the conditions and the outcome. This study focuses on the intermediate solution, and results are presented according to this type of solution. This solution balances comprehensiveness and interpretability, avoiding the potential oversimplification of the parsimonious solution and the complexity of the complex solution. By incorporating theoretically informed assumptions, the intermediate solution provides a manageable set of causal configurations that are both empirically grounded and theoretically meaningful. Table 4 outlines the specific assumptions employed to derive the intermediate solution--the estimated relationships or expectations between causal conditions and a situation of high propensity for business failure. The intermediate solution is derived by incorporating theoretically informed assumptions to identify causal configurations that are both empirically and theoretically meaningful. The \"Positive\" and \"Negative\" signs in Table 4 reflect these theoretically informed assumptions.