## 4 Results
### Stage 2  
In accordance with the adopted methodology of analyzing the results of the study, in the second stage, the impact of the selected characteristics of the enterprise (presented in a detailed analysis of the research sample--Section 3) on the use of solutions assigned to trends affecting the development of the operational processes of these enterprises will be checked. The degree of utilization of the solutions is visualized using a box plot (Figure 5). By analyzing Figure 5, it can be concluded that the use of solutions assigned to both trends is at a similar level. The purpose of the analysis, however, was to show the characteristics of manufacturing companies that affect the degree of use or perception of solutions assigned to individual trends. In order to demonstrate these relationships, it is necessary to carry out group comparisons. The choice of the statistical method by means of which the comparison between the groups will be made is possible after checking the normality of the distribution of results for each of the trends. The results of the analysis of the normality of the distribution, carried out using the Anderson-Darling method, are presented in Table 6.  
Figure 4: PLS path model. The results of the test of normality of distribution require the rejection of the null hypothesis (according to the Anderson-Darling method) regarding the normality of the distribution of responses in digitalization trends and software development trends. The distributions of results for both trends are, therefore, not normally distributed. In comparing results, it is, therefore, necessary to use a non-parametric method to verify statistical hypotheses. They analyze the results for individual strata of the research sample (separated strata in accordance with the adopted characteristics of production enterprises); they do not have uniform distributions. This means that the [PERSON]'s median test will be used to verify the statistical hypotheses. According to this method, the following statistical hypotheses were adopted regarding group comparisons:  
**Hypothesis 0 (H0). **_The population medians are all equal._  
**Hypothesis 1 (H1). **_The population medians are not all equal._  
The interpretation of the test results is as follows:  
* \(p\)-value \(\leq\)\(\alpha\): The differences between some of the medians are statistically significant. * \(p\)-value \(>\)\(\alpha\): The differences between the medians are not statistically significant. A significance level \(\alpha\) = 0.05 was assumed in this study. The group comparison results are presented in Table 7. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Digitalization Trends** & **Software Development Trends** \\ \hline Anderson–Darling test & \textless{}0.005 * & \textless{}0.005 * \\ \(p\)-value & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Anderson–Darling normality test results—development trends. Figure 5: Box plot: comparison of the importance of the analyzed trends for manufacturing companies. *, ** denotes single observations. Comparing between the groups allows us to conclude that the characteristics of the company affect only the absorption and use of solutions included in the digitalization trends. In addition, only selected characteristics are important, such as the type of the company and the age of the company. The characteristics of the company are not important when using solutions in the field of software development trends. Knowing that the distribution of results is not normal, the medians of the results were compared. The results of using solutions in the field of digitalization trends, depending on the type of a company, are presented in Table 8.  
Knowing the medians of the results of the use of solutions in the field of digitalization trends for enterprises of a specific type, we checked for which types the use of these trends differed in a statistically significant way. For this purpose, pairwise comparison and Mood's median test were used.