## 5 Results and Robustness Analysis
### Results
This notation is frequently employed in fsQCA studies to denote the non-existence or lack of a particular factor or condition within a given configuration. By utilizing this symbol, we can effectively distinguish between the presence and absence of conditions, allowing for a more precise analysis of the complex relationships between variables and outcomes. In fuzzy-set qualitative comparative analysis (fsQCA), causal conditions are classified as either core or peripheral based on their relative importance and stability across different solution types [45; 53]. Core conditions are those that consistently appear in both parsimonious and intermediate solutions, demonstrating a robust and central influence on the outcome. Their inclusion is critical; omitting a core condition generally leads to significant alterations in the overall configuration, underscoring its essential role in the causal recipe. Peripheral conditions, by contrast, tend to emerge only in the intermediate solution. They are considered less central and often represent context-specific factors or modifiers that enrich the explanation of the outcome without being indispensable. While peripheral conditions contribute to the complexity of the causal pathways, their absence does not typically disrupt the fundamental structure of the solution. This distinction between core and peripheral conditions enables the explanation of the most critical causal factors and those that offer additional contextual insights [45; 53]. To enhance the clarity of the findings, we have transformed the solutions from Table 5 into a more reader-friendly table (Table 6). Conventionally, a black circle (\(\bullet\)) denotes the presence of a condition, a crossed-out circle (\(\otimes\)) indicates its absence or negation, and a blank space represents a \"do not care\" condition [63]. Additionally, core conditions are distinguished using large circles, while peripheral conditions are indicated with smaller circles. \begin{table}
\begin{tabular}{c c c} \hline \hline Solution: & \(\rightarrow\) -FSOLL*-F.TPR*-F.PPE & + F.GEAR*-F.TPR*-F.PPE + \\ \(\sim\)F.RTAS*-F.TPR*-F.PPE*F.TRUCAM & \(\rightarrow\) F.VADISP2 BB \\ \hline \hline \multicolumn{3}{c}{Consistency} & Coverage \\ \(\sim\)F.SOLL*-F.TPR*-F.PPE & 0.867 & 0.181 \\ F.GEAR*-F.TPR*-F.PPE & 0.734 & 0.085 \\ \(\sim\)F.RTAS*-F.TPR*-F.PPE*F.TRUCAM & 0.890 & 0.022 \\ Total minimum & 0.802 & 0.223 \\ \hline \hline \end{tabular} Notes: Sample size = 38,456. The symbol – indicates the absence of the condition (or low values), and its omission indicates its presence (or high values of the condition). The asterisk symbol (*) represents the logical “and” operator, while the plus symbol (+) represents the logical “or” operator, consistent with standard Boolean algebra. “F.” denotes that variables have been transformed into conditions (fuzzified). Variable definitions are provided in Table 3.  
\end{table} 
Table 5: Sufficiency analysis of conditions for high propensity for failure. The three solutions identified reveal a consistent pattern: Low values for both results per employee and profit per employee are associated with a high propensity for business failure. This suggests that poor financial performance, as indicated by these ratios, is a key determinant of business failure. Notably, one of the solutions includes the environmental impact variable, which is considered a core condition. This finding indicates that high environmental risk can contribute to a higher propensity for business failure, highlighting the potential impact of environmental performance on a company's financial viability (see third causal configuration in Tables 5 and 6). Therefore, one of the three identified solutions highlights the crucial role of the environmental condition. The presence of high values for this variable is associated with an increased propensity for business failure.