## 4 Results Analysis  
This study examines the primary factors influencing users' continued use of digital interactive installations in the Capital Museum from seven aspects: learner factors, peer factors, environmental factors, perceived ease of use, perceived usefulness, perceived enjoyment, and satisfaction, as well as the intrinsic relationships among these elements. The findings provide reference suggestions for the design optimization and operation of digital interactive installations in the Capital Museum. Based on the above data analysis, 7 out of the 10 hypotheses were validated. The following discussion addresses the analytical results. Table 7 shows the test results of each hypothetical path in the Bayesian structural equation model. Analysis shows that most of the hypotheses are supported: H1 (LF\(\rightarrow\)PEU, 1.158), H3 (PF\(\rightarrow\)LF, 1.110), H4 (EF\(\rightarrow\)PF, 1.238), H5 (PEU\(\rightarrow\)PU, 1.037), H7 (PEU\(\rightarrow\)SA, 1.072), H8 (PU\(\rightarrow\)WTCTU, 0.271), H9 (PE\(\rightarrow\)WTCTU) The path coefficients of 0.223) and H10 (SA\(\rightarrow\)WTCTU, 0.530) were both significant (the 95% confidence interval did not include 0), and the Rhat value was close to 1, indicating that the model converged well. It is worth noting that the EF\(\rightarrow\)PF path (H4) has the strongest effect (1.238), but the posterior standard deviation is relatively large (0.143), suggesting that there may be certain uncertainties. In contrast, the confidence intervals of H2 (LF\(\rightarrow\)WTCTU, 0.017) and H6 (PEU\(\rightarrow\)WTCTU, 0.355), which contained 0 values, failed the test. The core findings show that: (1) The learner factor has a significant indirect impact on the willingness to use through PEU; (2) Satisfaction (SA) has the strongest predictive power for willingness to continue using (WTCTU) (0.530); (3) The path results involving WTCTU show differentiation. Among them, the direct effects of PU and PE are significant, but the direct effects of LF and PEU are not significant. This suggests that WTCTU may be driven more by experiential factors (such as SA) rather than basic cognitive factors. It is suggested that subsequent studies can modify the model for non-significant paths (such as H2, H6) or add moderating variable analyses Figure 5.  
This Table 8 presents the analysis results of direct effects, indirect effects, and total effects in the Bayesian structural equation model. In terms of direct effects, PE\(\rightarrow\)WTCTU (0.223, 95% CI [0.114,0.332]) showed a significant positive impact. Indirect effect analysis showed that the two paths, EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)WTCTU (0.023, 95% CI [\(-\)0.297,0.342]) and EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)WTCTU (0.565, 95% CI [\(-\)0.172,1.302]), were not significant. However, the two paths of EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)SA\(\rightarrow\)WTCTU (0.904, 95% CI [0.482,1.325]) and EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)PTCTU (0.447, 95% CI [0.140,0.755]) had significant positive effects. The total effect analysis indicated that the total effect (0.223) of PE\(\rightarrow\)WTCTU was consistent with the direct effect, while the total effect produced by EF\(\rightarrow\)WTCTU through multiple mediating paths was the strongest (1.939, 95% CI [1.522,2.356]). These results reveal three important findings: (1) Perceived entertainment (PE) has a di