## 5 Discussion  
### General Issues  
This study, framed within the Theory of Planned Behavior (TPB), analyzes data from a national survey conducted in Spain by the Centro de Investigaciones Sociologicas (Sociology Research Centre) [15], focusing on the perception and use of generative artificial intelligence (GAI). To this end, advanced machine learning techniques were applied. This study had two main objectives. The first (RO1) was used to assess the validity of the proposed theoretical model for fitting the sample data and predicting the observed behavior. The results show that combining Spearman correlations with machine learning models such as decision trees (DTR) and their extensions (RF and XGBoost) not only enables the visualization of interactions between explanatory variables, but also allows for the explanation of approximately 95% of the variance in the dependent variable. Furthermore, the model demonstrated predictive capacity, regardless of the fitting methodology used. However, RF emerged as the model with the highest predictive power. The application of SHAP values to the best-performing model (Random Forest) enabled the identification of the most influential factors in explaining GAI use, addressing RO2. The main drivers, in order of importance, are knowledge about technology (KNOW), innovativeness (INNOV), membership in older generations (GEN), and perceived usefulness, both in social utility terms (SU) and in terms of performance expectancy (PE). Conversely, factors such as the perceived need for stricter regulations, negative societal externalities, and privacy risk perception show lower relevance. Additionally, sociodemographic \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean** & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \hline
**Variable** & **SHAP** & **SHAP\_Lower** & **SHAP\_Upper** \\ \hline KNOW & 0.559 & 0.547 & 0.571 \\ GEN & 0.297 & 0.293 & 0.301 \\ INNOV & 0.193 & 0.189 & 0.197 \\ SU & 0.154 & 0.150 & 0.158 \\ ED & 0.136 & 0.134 & 0.138 \\ REG & 0.117 & 0.113 & 0.120 \\ PE & 0.102 & 0.099 & 0.104 \\ PRI & 0.074 & 0.072 & 0.076 \\ SR & 0.073 & 0.071 & 0.075 \\ IN & 0.053 & 0.051 & 0.054 \\ SEX & 0.039 & 0.038 & 0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Relative importance of explanatory variables to predict USE measured with the mean of the absolute value of SHAP. graphic variables such as education level, income, and gender have a limited influence in predicting GAI use. The finding that KNOW is the most important variable reinforces existing evidence of its relevance to AI adoption--not only in domains such as healthcare [58; 59] and education [40; 43; 60], but also across diverse cultural contexts, including Abu Dhabi [40], Taiwan and Indonesia [43], Germany [60], and India [59]. In the field of AI adoption, attitude has been consistently reported as a key determinant of acceptance and use [17], which supports the strong role of INNOV, the significance of which has been observed in multiple empirical studies in countries such as Germany [20], Israel [46] and Croatia [47]. This also helps explain the notable relevance of PE, consistently confirmed across various GAI application contexts [37; 38; 39; 40; 41; 42; 43], including regions and countries such as Southern Europe [37], Germany and Poland [38; 42], Abu Dhabi and Saudi Arabia [40; 41], and Southeast Asia [43]. The strong impact of SU and REG, both related to subjective norms, is consistent with the idea that societal perceptions of AI use are important for understanding its acceptance, as shown in previous studies placed in different cultural context such as Germany [20], Croatia [47], Arabia Saudi [62], or Poland and Egypt [63]. The results concerning privacy risk perceptions were unexpected. In our sample, privacy risk showed a positive correlation with USE in bivariate analysis and appeared as both a positive and negative predictor in the DTR.