## 6 Results
### Assessment of the Research Model
#### 6.2.2 Assessment of the Structural Model  
Bootstrapping was performed using 500 samples to determine the significance of the paths within the structural model. Aside from the single path tests, the amount of variance explained by the independent variables, gauged using R2 of the dependent ones, which revealed that independent variables collectively explained 86% of the variance in behavioural intention towards smart meter system use. The next section discusses the  
Figure 4: Measurement model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Construct** & **Composite Reliability (CR)** & **Average Variance Extracted (AVE)** \\ \hline Performance expectancy & 0.982 & 0.947 \\ Effort expectancy & 0.985 & 0.942 \\ Environmental awareness & 0.985 & 0.943 \\ Facilitating conditions & 0.978 & 0.918 \\ Habit & 0.979 & 0.938 \\ Eco-effective feedback & 0.981 & 0.945 \\ Privacy concerns & 0.985 & 0.942 \\ Social influence & 0.975 & 0.93 \\ Technology awareness & 0.963 & 0.837 \\ Electricity saving knowledge & 0.988 & 0.943 \\ Behavioral intention & 0.981 & 0.945 \\ Use behaviour & 0.988 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of PLS Analysis: Measurement Model. hypotheses results one by one. The results of the hypotheses testing for each hypothesis are interpreted in relation to the relevant studies in the literature. This study made use of the PLS-SEM indicator method to conduct an estimation of the strength of the moderating effects of experience on effort expectancy, facilitating conditions, social influence, eco-effective feedback, habit, and privacy concerns. In order to determine such moderating effects, [PERSON]'s [PERSON] (1984) guidelines were followed for effect size. The proceeding figures and table presents the moderating effect on the relationship between the independent and dependent variables. Under the moderating effects hypotheses, the experience was hypothesized to moderate the effects of effort expectancy, facilitating conditions, habit and privacy concerns on behavioural intention, and the effects of habit on technology use. Hypothesis H1 was supported by the results in that experience moderated the effort expectancy-behavioural intention relationship (\(\beta=-0.027\), \(p=0.003\)). Along a similar line of findings, experience also moderated the habit-behavioural relationship (\(\beta=0.019\), \(p=0.006\)) in H4, and the habit-technology use relationship (\(\beta=-0.072\), \(p=0.011\)) in H5. The findings of the privacy concerns-behavioural intention relationship (\(\beta=-0.017\), \(p=0.000\)) in H6 was supported. In contrast, the experience did not moderate effect of facilitating conditions on behavioural intention, and the result was found insignificant. Practically, the magnitude and effect size of the moderating effects were small. In H3, the moderation effect was not considered since the social influence-behavioural intention relationship was not significant. The hypotheses results (H1-H6, excluding H3) are provided in Table 5.  
In Figure 5, it is evident that the effort expectancy-behavioural intention relationship is direct with higher levels of the former expected to lead to higher levels of the latter. Added to this, the moderating effect scope was examined in detail. The upper blue line represents low level of moderator construct (experience), while the green line represents a high level of experience, with a steeper slope. This is logical with a negative interaction effect. Therefore, the simple plot supports the prior discussion on the negative interaction term, where higher levels of experience are expected to lead to lower relationship strength between effort expectancy and behavioural intention.