## 5 Results' Analysis  
### Model Measurement  
The data analysis was conducted using the two-step method developed by [PERSON] and [PERSON] [86]. Initially, an assessment was conducted to ascertain the convergent validity and discriminant validity of the measurement model. Subsequently, the research hypotheses and structural model framework were tested.  
Firstly, given the data collection method used in this study, [PERSON]'s single-factor test was implemented to evaluate the likelihood of common method bias (CMB) in the self-reported survey data [87]. The model measurement performed a non-rotational ex  
Figure 3: Analytical method process.  
ploratory factor analysis on all measurement items and found that the first latent factor explained 33.846% of the total variance, which was far below the conservative benchmark of 40-50% representing the critical CMB. In addition, the total variance extracted by all factors reached 72.253%, indicating that the explanatory power of the remaining constructs was evenly distributed. These findings support the absence of a single factor dominating the covariance structure of the data, which represents current diagnostic evidence supporting the validity of subsequent structural analysis.  
Secondly, as illustrated in Appendix A, the measurement model demonstrates notable robustness with regard to its quality. The reliability of the internal consistency is demonstrated by [PERSON]'s \(\alpha\) values, ranging from 0.856 (CUI) to 0.929 (PU), which exceed the standard of 0.70, indicating good internal consistency. The composite reliability (CR) also ranged from 0.864 to 0.935, thereby reinforcing the evaluation of reliability based on [PERSON]'s alpha values and confirming the consistency and reliability of the variables. Meanwhile, convergent validity was further confirmed by the average variance extracted (AVE) values, which range from 0.562 (CUI) to 0.743 (PU) and all exceed the 0.50 standard. The elevated loadings in the data results suggest a strong indication of validity for these items [88].  
Thirdly, the structural model demonstrates an exceptional overall fit to the data (Table 2) [89]. The normed chi-square statistic (CMIN/DF = 1.378) is significantly below the conservative critical value of three, suggesting that the difference in degrees of freedom is acceptable. The absolute fit indices (root mean square residual (RMR) and goodness-of-fit index (GFI)) and incremental fit indices (Tucker-Lewis index (TLI) and comparative fit index (CFI)) all met the recommended standards. Furthermore, the parabolic correction fit was substantiated by the root mean square error of approximation (RMSEA = 0.025), which is below the strict 0.05 threshold, thereby confirming the model's fit.  
Finally, Appendix A presents the descriptive statistics and inter-structural correlation matrices. The square root of the average variance extracted (AVE) is displayed on the diagonal. For each latent variable, the diagonal coefficients exceed all off-diagonal correlation coefficients with other constructs, thereby meeting the Cornell-Larcker criteria and confirming the discriminant validity of all theoretical dimensions [90]. Additionally, the correlation between structures was moderate (\(\left\lvert\,\mathrm{r}\,\right\rvert\leq 0.531\)) and only statistically significant in the theoretical prediction case, indicating that multicollinearity is unlikely to interfere with parameter estimation. The association between demographic covariates and core structures was negligible (\(\left\lvert\,\mathrm{r}\,\right\rvert\leq 0.096\)), suggesting that structural relationships are not spuriously driven by respondents' background characteristics. These diagnostic results confirm that each potential structure captures the unique experience of university students using LLMs to assist in online consumption of sustainable products, thereby laying a solid foundation for subsequent hypothesis testing of structural models.