# Supply Chain Management Maturity and Business Performance: The Balanced Scorecard Perspective
## 5 Results
### Moderating Role of Industry Characteristics for Testing Hypothesis H5-H6  
The moderating effect in PLS-SEM is determined by assigning values based on the construct. To determine a moderation effect of a relationship between an independent variable (IV) and a dependent variable (DV), which is moderated by a third variable, a regression analysis needs to be performed, and then the values from the independent variables, a moderator variable, and values from their interaction or the product valuable are used. A moderator effect can be present if there is a statistically significant change between two groups with respect to the \(\chi 2\) value. Figure 2 indicates the existence of a significant relationship between the variable TECH_DYN and BSC (Financial perspective, Customer perspective, Perspective of innovation and learning, and Perspective of internal processes). In contrast, the relationship with the variable STATE is not significant. Therefore, we focus solely on the moderating role of TECH_DYN. The moderation effect of variable TECH_DYN on the relationship between SCMM and BSC is presented in Table 8. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Construct** & **FOND** & **STRUCT** & **VISION** & **INTEGR** & **DYNAM** & **FINANCE** & **CUSTOM** & **INNO** & **INT** & **TECH** & **DTYN** & **STATE** \\ \hline FOND & 0.532 & & & & & & & & & \\ STRUCT & 0.518 & 0.667 & & & & & & & \\ VISION & 0.453 & 0.556 & 0.687 & & & & & & \\ INTEGR & 0.483 & 0.551 & 0.550 & 0.665 & & & & & \\ DYNAM & 0.459 & 0.477 & 0.550 & 0.622 & 0.656 & & & & \\ FINANCE & 0.114 & 0.076 & 0.086 & 0.106 & 0.131 & 0.823 & & & \\ CUSTOM & 0.335 & 0.257 & 0.207 & 0.272 & 0.387 & 0.370 & 0.726 & & \\ INNO\_LEARN & 0.377 & 0.338 & 0.346 & 0.442 & 0.422 & 0.149 & 0.353 & 0.598 & & \\ INT\_PROC & 0.297 & 0.197 & 0.202 & 0.219 & 0.344 & 0.258 & 0.645 & 0.371 & 0.723 & & \\ TECH\_DYN & 0.083 & 0.132 & 0.084 & 0.106 & 0.111 & 0.188 & 0.211 & 0.140 & 0.208 & 0.690 & \\ STATE & 0.026 & 0.047 & 0.043 & 0.063 & 0.014 & 0.014 & 0.031 & 0.055 & 0.035 & 0.057 & 0.514 \\ \hline \hline \end{tabular} Note: Squared correlations; AVE in the diagonal. Source: Author’s calculation using Smart PLS; Source: Author’s work, which was produced using Adanco 2.3.2.  
\end{table} 
Table 6: Discriminant validity testing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hypothesis—Effect** & **Path Coefficient** & **Standard Error** & **t-Value** & \(p\)**-Value** \\ \hline H1—SCMM \textgreater{} FINANCE & 0.231 & 0.079 & 2.939 & 0.003 *** \\ H2—SCMM \textgreater{} CUSTOM & 0.509 & 0.072 & 7.052 & 0.000 *** \\ H3—SCMM \textgreater{}> & 0.644 & 0.050 & 12.845 & 0.000 *** \\ INNO\_LEARN & & & & & \\ H4—SCMM \textgreater{} INT\_PROC & 0.460 & 0.068 & 6.730 & 0.000 *** \\ \hline \hline \end{tabular} Note: *** statistically significant at 1%; Source: Author’s work, which was produced using Adanco 2.3.2.  
\end{table} 
Table 7: Hypotheses testing. The product variable needs to be significant to be considered a moderating variable. H5a, H5b, H5c, and H5d examine the industry's moderating effects on a company's performance (assessed by the BSC framework). Figures 3-6 indicate that the linear regression trend is an adequate proxy for the moderating equations, which was confirmed by testing several other forms, such as exponential and linear. Figure 4: Moderating effect of industry on customer perspective of BSC: Source: Author’s work, which was produced using SPSS 28.0.0. Figure 5: Moderating effect of industry on innovation and learning perspective of BSC; Source: Author’s work, which was produced using SPSS 28.0.0. Figure 3: Moderating effect of industry on financial perspective of BSC; Source: Author’s work, which was produced using SPSS 28.0.0. H5a is based on a significant positive interaction of TECH_DYN with the relation between SCMM and FINANCE. According to the model and Table 8, the R\({}^{2}\) for the regression without an interaction term is 0.231.