## 4 Results  
### Stage 1  
The aim of the first stage of the analysis of the results of the study was the identification of trends affecting the development of operational processes of production companies. To test the research model and proposed hypothesis, this study applies partial least squares path modeling (PLS), a variance-based structural equation modeling technique (SEM) that aims to maximize the explained variance of the dependent latent constructs [41]. SmartPLS version 3 was used to analyze the data in this study, following a two-step analysis approach. Due to the characteristics of the studied variables, it was necessary to check them for construct reliability and discriminant validity. [PERSON]'s alpha (\(\alpha\)) was used to measure construct reliability. Average variance extracted (AVE) has often been used to assess discriminant validity. The results of the [PERSON]'s alpha (\(\alpha\)) study are shown in Figure 1. P-Value marked in green means that the model meets the criteria and has sufficient internal consistency.  
When evaluating construct reliability and validity, it should be noted that the [PERSON]'s Alpha value for each construct was greater than 0.7 [41]. It can be argued that the results demonstrate internal consistency and reliability.  
Average variance extracted (AVE) was used to assess discriminant validity. The results of the AVE study are shown in Figure 2. P-Value marked in green means that the model meets the criteria and can be considered a high-quality model.  
The average variance extracted (AVE) is above the recommended threshold of about 0.5 or higher [41], demonstrating an acceptable level of discriminant validity.  
Before proceeding with the assessment of the structural model, the coefficient of determination, R2, was calculated for each endogenous latent variable. This coefficient determines the predictive power of the model and must be greater than 0.1 [42]. R2 values for the developed model are presented in Figure 3. P-Value marked in green means that the model is well matched to the empirical data for a given criterion. (digitalization trend). The red color of the P-Value indicates that the fit is unsatisfactory.  
Figure 1: Quality criteria: [PERSON]’s Alpha of the model.  
Figure 3: Quality criteria: R2 of the model.  
Figure 2: Quality criteria: AVE of the model.  
For one endogenous latent variable, innovative solutions in logistics, the coefficient of determination's R2 value was below the recommended level of \(0.1\). This indicates that the results for that latent variable have low predictive power.  
A PLS path model with four latent variables (identified in the literature review and checked in the survey study) is presented in Figure 4.  
The model presented in Figure 4 shows the relationship between latent variables with path coefficients and \(p\)-values. It shows that process development trends in production companies are directly dependent on software development trends and, indirectly, digitalization trends. Innovative solutions in logistics have no influence on process development trends. Due to the adopted methodology of analyzing the results, only two research areas will be subject to a detailed analysis in the second stage:  
* Digitalization trends,
* Software development trends.