## 3 Results and Discussions
### The Structural Model  
The use of PLS algorithms allowed the evaluation of the structural relationships between the model constructs and the testing of research hypotheses. Table 5 shows the results of the correlations between the construct variables, values of the structural coefficients Path (\(\beta\)) and \(t\)-value correspondence (T-Statistics). \(p\)-values, R\({}^{2}\) and Q\({}^{2}\), after using the Bootstrapping and Blindfolding options (per 1000 copies) within SmartPLS. The evaluation of the structural model included three distinct stages. In the first stage, the impact of exogenous variables was evaluated: Effort Expectancy, Attitude Towards IoT, Perceived Autonomy, Perceived Competence and Perceived Relatedness on the endogenous factor Intrinsic Motivation. Path coefficients (\(\beta\)) highlight the intensity of the links between the construct variables and their size must vary between 0 and 1 [121]. Loads of PR (0.231 \(>\) 0), EE (0.175) and PA (0.123) pathways show positive but low correlations in relation to IM (Table 5). A negative correlation was identified between the latent variables PC and IM (\(\beta=-0.010<0\)). The calculation results for the correlations EE- \(>\) IM (2407), PA- \(>\) IM (2484) and PR- \(>\) IM (2031) indicate t-values over 1.96, significant at the probability level of 0.05. The situation is reconfirmed by \(p\)-values which once again highlights that the three paths are significant at a level higher than the probability level 0.001. The AT- \(>\) IM and PC-IM \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hypothesis** & **Correlation** & \(\beta\) & **T-Statistics** & \(p\)**-Values** & **Decision** \\ \hline H\({}_{1}\) & EE - \(>\) IM & 0.175 & 2.407 & 0.016 & Supported \\ H\({}_{2}\) & AT - \(>\) IM & 0.079 & 1.186 & 0.236 & No Supported \\ H\({}_{3}\) & PA - \(>\) IM & 0.123 & 2.484 & 0.013 & Supported \\ H\({}_{4}\) & PC - \(>\) IM & \(-0.010\) & 0.110 & 0.912 & No Supported \\ H\({}_{5}\) & PR - \(>\) IM & 0.231 & 2.031 & 0.043 & Supported \\ H\({}_{6}\) & PE - \(>\) EM & 0.136 & 2.793 & 0.024 & Supported \\ H\({}_{7}\) & SI - \(>\) EM & 0.139 & 3.426 & 0.001 & Supported \\ H\({}_{8}\) & FC - \(>\) EM & 0.044 & 0.968 & 0.333 & No Supported \\ H\({}_{9}\) & PP - \(>\) EM & \(-0.004\) & 0.057 & 0.954 & No Supported \\ H\({}_{10}\) & PS - \(>\) EM & 0.590 & 6.759 & 0.000 & Supported \\ H\({}_{11}\) & EM - \(>\) BI & 0.180 & 1.511 & 0.131 & No Supported \\ H\({}_{12}\) & EM - \(>\) IM & 0.344 & 4.149 & 0.000 & Supported \\ H\({}_{13}\) & IM - \(>\) BI & 0.542 & 5.879 & 0.000 & Supported \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results. Figure 7: The variance inflation factor (VIF). paths that showed \(t\)-values of 1.186 and 0.011 (\(<\) 1.96), respectively, are discordant, being insignificant at the level of 0.05. The second stage aimed at determining the impact of the factors: Performance Expectancy (PE), Social Influence (SI), Facilitating Conditions (FC), Perceived Privacy (PP) and Perceived Security (PS) on Extrinsic Motivation (EM). In relation to the endogenous variable EM (Table 5) correlations were obtained: weak with the variables PP (\(-\)0.004 \(>\) 0) and FC (0.044), moderate with the factors SI (0.139), PE (0.136) but also high with PS - \(>\) MS (0.590 \(>\) 0). In this part of the construct the pathways PS - \(>\) EM (6.759 \(>\) 1.96), SI- \(>\) IM (3.426) and PR- \(>\) IM (2.301 \(>\) 1.96) proved to be positive and significant. Although insignificant, the PP -\(>\) EM (0.057) pathway indicates a negative impact compared to FC - \(>\) EM (0.968 \(<\) 1.96) which exerts an extremely weak but positive influence. The third stage aimed at determining how intrinsic and extrinsic motivation influenced the behavioral intention to use IoT solutions by Romanian managers at work. Table 6 shows two strong correlations between the variables IM and BI (0.542) as well as between EM and IM (0.344) were. A positive but moderate correlation was identified between EM and BI (0.180 \(>\) 0).