## 4 Results Analysis
### Summary and Outlook  
This study constructs a factor model influencing the continued use of digital interactive installations at the Capital Museum, based on distributed cognition theory and perceived value theory, and employs Bayesian Structural Equation Modeling (Bayesian-SEM) for data analysis.  
The research finds that perceived ease of use has a direct predictive effect on users' willingness to continue using, while environmental factors and peer interaction indirectly affect user behavior through learners' participation. User satisfaction is the core mediating variable between perceived ease of use and the willingness to continue using. It is worth noting that perceived usefulness and entertainment do not show a direct impact, indicating that in this situation, convenience and social experience are more important than functional benefits. These results provide a theoretical basis for the design optimization of digital interactive devices in museums, emphasizing the importance of interface friendliness, the creation of a collaborative environment, and the improvement of user satisfaction.  
Figure 5: Hypothetical model results (B-SEM).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Effect Type** & **Path** & **Estimate** & **Post SD** & **CI** & **Significance** \\ \hline
**Direct Effect** & \begin{tabular}{c} PE\(\rightarrow\)WTCTU \\ \end{tabular} & 0.223 & 0.056 & [0.114, 0.332] & YES \\ \hline \multirow{4}{*}{**Indirect Effect**} & \
\begin{tabular}{c} EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)WTCTU \\ \end{tabular} & 0.023 & 0.163 & [\(-\)0.297, 0.342] & NO \\  & EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)WTCTU & 0.565 & 0.376 & [\(-\)0.172, 1.302] & NO \\  & EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)SA\(\rightarrow\)WTCTU & 0.904 & 0.215 & [0.482, 1.325] & YES \\  & EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)WTCTU & 0.447 & 0.157 & [0.140, 0.755] & YES \\ \hline \multirow{2}{*}{**Total Effect**} &
\begin{tabular}{c} PE\(\rightarrow\)WTCTU (Total) \\ \end{tabular} & 0.223 & 0.056 & [0.114, 0.332] & YES \\  & EF\(\rightarrow\)WTCTU (Total) & 1.939 & 0.213 & [1.522, 2.356] & YES \\ \hline \hline \end{tabular}
\end{table}
Table 8: Bayesian estimates of direct, indirect, and total effects for the hypothesized model.  
Future research can be further expanded in the following directions: Firstly, expand the sample range to cover users of different types of museums (such as art galleries and science and technology museums) to verify the universality of the conclusion; Secondly, introduce longitudinal tracking research to dynamically observe the long-term evolution law of user behavior; In addition, explore the influence of individual differences (such as age and cultural background) on the interactive experience to formulate more targeted service strategies. From a technical perspective, multi-sensory fusion and intelligent measurement technologies can be combined to optimize the immersion and personalization of interaction design. Finally, attention should be paid to data ethics and privacy protection to ensure the sustainability of technology applications in cultural dissemination. Through multidisciplinary integration and empirical innovation, promote the paradigm transformation of museum digitalization from \"technology-driven\" to \"humanity-technology collaboration\", and ultimately achieve in-depth empowerment of cultural heritage education.  
Based on the findings, the following recommendations are proposed: