## 4 Results
### Proposed Metrics' Results  
The analysis identified the four components that explain 100% of the total variance in user satisfaction. The first component, accounting for 61.44% of the variance, is heavily weighted toward effectiveness measures (both those inherent in usability assessment and additional specific metrics), reflecting the importance of this dimension in overall system performance, indicating that task accuracy and reliability are critical drivers of user satisfaction. The second component, which explains 27.52% of the variance, is strongly associated with CUQ scores, emphasizing the importance of usability and navigability in shaping the user experience. The third component, explaining 11.02% of the variance, is primarily related to the role of UES-SF engagement in user satisfaction. The fourth component, with a minimal variance explanation of 0.02%, shows a negligible impact and is likely an artifact of the lower-order effects.  
Based on these results, we propose a chatbot composite UX metric that integrates task-related effectiveness with user-centered usability. This metric was constructed by weighting effectiveness more heavily than the CUQ to reflect their contributions to the explained variance and their roles in shaping satisfaction. We determined the specific weights by applying a variance-weighted average approach, and the influence of each component was adjusted according to its proportion of the total explained variance. Specifically, we calculated the weight of each metric by multiplying each component's variance by the  
Figure 5: Effectiveness and efficiency of the chatbot.  
metric's loading within that component. The combined weights from all four components were then normalized to ensure that they summed to one, resulting in final weights of approximately 0.696 for effectiveness, 0.267 for CUQ, 0.073 for UES-SF, and \(-\)0.036 for efficiency, as shown in Table 9.  
In practice, one may choose 80% of the variance and stop; however, we show the complete component effect for a comprehensive solution. This approach reflects the importance of task performance and usability, while recognizing the supportive roles of engagement and efficiency in overall user satisfaction. The negative weight for efficiency likely indicates that higher efficiency scores in this context were inversely related to perceived satisfaction, suggesting that users might have prioritized accuracy and usability over response speed alone.  
Although all four metrics contribute to the satisfaction score, these findings suggest that the CUQ and effectiveness are the most significant indicators of user satisfaction in Arabic chatbots. This statistically driven combination offers a comprehensive measure of satisfaction, capturing both the chatbot's task performance and the quality of the user experience. Thus, this combined metric provides a holistic measure of user satisfaction that integrates functional performance and usability, thereby allowing for an in-depth assessment of chatbot quality. These results indicate that the UES-SF and efficiency contribute relatively less to the overall UX in this chatbot, as reflected by their lower weights. This suggests that while engagement (UES-SF) and efficiency are part of the experience, the UX is heavily driven by the effectiveness and usability (CUQ) aspects in this context.