## 5 Results and Discussions  
The values of the coefficients of the structural model are estimated using the structural equations, and the values obtained could be seen in Figure 4. The value of R-square is 0.668, which means that 66.8% of the variability of the competition degree regarding the green acquisitions is explained by the variability of the model. Regarding the estimated values of the coefficients of the structural model, we observe that all are positive, which means that all latent variables have a positive impact on competition. Thus, of the six indicators, the variable with the greatest impact on competition is \"regulation\" (factor 0.712), followed by \"strategic vision\" (factor 0.658), \"degree of innovation\" (factor 0.622), operational management \"(factor 0.608), \"competencies\" (factor 0.584), and \"responsibility\" (factor 0.379). The reflective indicators of the dependent variable \"competition\" have values greater than 0.5, which means that the variables (conc_1, conc_2, and conc_3) are statistically significant. The high values of the reflective variables, namely \"number of competitors on the green procurement market\", \"price offered for green purchases versus reference price on the market\", and \"degree of concentration on the green procurement market\" show us that the degree of competition is well represented by the three indicators. In general, multiple factors improve the performance of the model and increase reliability compared to single factors. In our model, the dependent variable is composed of three variables. As with the formative variables, a reflective variable has a greater impact if it has high values of the coefficients, reflecting stronger ways of measurement. This is confirmed by the high values of the three coefficients of the dependent variables. In addition, to test the six statistical assumptions described in the previous chapter, a Bootstrap Test was performed on a sample of 300 respondents to generate the values of the \(t\) test (\(t\)-test) and the mean square deviations (standard deviation) of the model parameters. According to statistical theory, 'bootstrapping' allows the allocation of precision measurements to sample estimates. The results regarding the testing of statistical assumptions can be seen in Table 5. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Research Hypothesis** & **Coefficients** &
\begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & \(t\)**-Value** & \(p\)**-Value** \\ \hline H\({}_{1}\): Strategic Vision -\(>\) Green & 0.658 & 0.246 & 3.648 & 0.005 \\ procurement competition & 0.712 & 0.278 & 2.831 & 0.029 \\ H\({}_{2}\): Regulation -\(>\) Green & 0.584 & 0.202 & 3.289 & 0.009 \\ procurement competition & 0.608 & 0.209 & 2.405 & 0.036 \\ H\({}_{5}\): Responsibility -\(>\) Green & 0.379 & 0.247 & 1.789 & 0.242 \\ procurement competition & 0.622 & 0.248 & 3.234 & 0.008 \\ \hline \hline \end{tabular} Source: Data analysis was performed by the authors using the SmartPLS software 3.2.8.  
\end{table} 
Table 5: Results of statistical hypotheses tests. At a significance level of 95%, the assumptions for which the \(p\)-value is less than 0.05 are validated, while the others for which the \(p\)-value is greater than 0.05 are not. Thus, we can conclude that Hypotheses H1, H2, H3, H4, and H6 are valid, while H5 is not. These results confirm the economic studies according to which the strategic vision, the degree of regulation, the level of competences, the integrated operational management, and the degree of innovation have a significant impact on the competition in the green procurement [68] but denies that corporate social responsibility is an important factor in the degree of competition in green procurement [69; 70]. This leads to the conclusion that CSR is not well developed in Romania and a possible explanation for that is that the companies do not get enough incentives from the government for implementing CSR's in green procurement.