## 5 Results' Analysis
### ANN Results  
Similarly to the study by [PERSON] et al. (2013), the significant factors determined through SEM were used as the input neurons of the ANN model. To reduce the risk of overfitting, a highly consistent estimation accuracy was obtained in Table 5, and the model demonstrated consistently strong predictive performance. Consistent with recent SEM-ANN research [94], the relative importance of the predictors was evaluated through ten-fold cross-validation, where 90% of the data was used for training and 10% for testing prediction accuracy. This division maximizes the learning data while retaining an external subset for unbiased validation, and it has proven to be effective even in moderately sized samples. The sum squared error (SSE) of the training (N = 521-534) was 165.85 (SD = 6.50), and the root mean square error (RMSE) was 1.79 (SD = 0.04) [95]. The coefficients of variation for SSE and RMSE were 3.9% and 2.0%, respectively, indicating that the model learning process is stable and highly insensitive to random divisions of the observed values. The model displays consistent predictive capabilities across various test components [96], thus showcasing its robust and reproducible explanatory power. Although the test RMSE is significantly lower than the training RMSE, the narrow dispersion of the two metrics suggests that this difference is not a symptom of overfitting but rather reflects moderate distribution differences between partitions [95]. These insights can clarify the non-linear interactions of the antecedent factors driving the continued application of LLMs. As shown in Table 6, the results of the sensitivity analysis indicate the predictive ability of each input neuron. This study adopted the \"normalized importance\" procedure embedded in IBM SPSS 30.0 Neural Networks software, which is a connection weight method proposed by [PERSON] and [PERSON] [97]. For each fold of the 10-fold cross-validation, this algorithm decomposes the input-hidden-output weights, converts them into absolute values, and then readjusts the resulting contributions so that the most influential input reaches 100%. The standardized importance of the remaining neurons is obtained by dividing their original importance by this maximum value and expressing the quotient as a percentage [98]. Among the ten resampled ANN models, PU consistently emerged as the most significant predictor of university students' intention to continue using LLM-assisted online consumption tools (mean importance = 0.202, 86.7% of the maximum weight), followed by PEU (0.194, 85.7%). GSV (0.167, 73.7%) indicates that experiential satisfaction reinforces core technology acceptance cognition but does not completely replace its role. The findings indicate that PEV (0.142, 61.5%) and IQV (0.123, 54.5%) contributed moderately to incremental power. However, TTV (0.090, 40.3%) and GPV (0.083, 34.5%) exhibited the lowest rankings, suggesting that their direct influence on network prediction outcomes is comparatively negligible. The low inter-run variability of PU and PEU underscores the stability of their dominant position. Conversely, the high variability of TTV and GPV signifies that their relevance is context-dependent. As illustrated in Table 7, an evidence-based analysis is presented, utilizing both SEM and ANN methodologies. This comprehensive approach facilitates a nuanced evaluation of the factors motivating university students' sustained utilization of LLMs for the procurement of sustainable products. The extant literature on the subject indicates that both methods consistently agree that technology acceptance cognition holds a primary position. The strong consistency in ranking, particularly the joint dominance of PU, PEU, and GSV, validates the effectiveness of the theoretical model across various analytical paradigms. Subtle changes in ranking underscore capacity of ANN capacity to unveil potential non-linear or synergistic contributions, such as the heightened significance of PU.